{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            y      x0      x1      x2      x3      x4      x5      x6      x7  \\\n",
      "0     11.6948  0.0237  0.0445  0.4061  0.8424  0.4214  0.6728  0.8133  0.0923   \n",
      "1      9.8253  0.4341  0.6407  0.2565  0.2035  0.1256  0.4701  0.9650  0.1066   \n",
      "2     23.3586  0.1765  0.8790  0.6804  0.8298  0.0002  0.8823  0.6411  0.6149   \n",
      "3     13.5047  0.8928  0.3574  0.1590  0.6387  0.9506  0.1529  0.3225  0.0040   \n",
      "4     17.3522  0.8197  0.6020  0.3762  0.9833  0.4994  0.5512  0.0219  0.2650   \n",
      "5     19.3476  0.9363  0.1676  0.5298  0.7799  0.0563  0.4845  0.6672  0.8950   \n",
      "6     14.9536  0.3711  0.2059  0.7562  0.6672  0.7035  0.0948  0.6964  0.6683   \n",
      "7     15.9923  0.9119  0.4102  0.5619  0.4864  0.1560  0.2987  0.0668  0.9284   \n",
      "8     13.4537  0.7834  0.3045  0.0486  0.8205  0.2590  0.5412  0.0025  0.7891   \n",
      "9      9.4677  0.0049  0.8610  0.2372  0.2630  0.3625  0.3093  0.3734  0.5695   \n",
      "10    17.7552  0.7772  0.5735  0.4634  0.3603  0.6493  0.2141  0.6861  0.5543   \n",
      "11    17.7484  0.9776  0.1901  0.4582  0.9996  0.1971  0.8638  0.1981  0.0861   \n",
      "12    19.8004  0.6582  0.3762  0.9340  0.4968  0.9529  0.7160  0.5707  0.4588   \n",
      "13    21.2639  0.8919  0.4975  0.3943  0.6361  0.3295  0.9285  0.4932  0.3647   \n",
      "14    15.3692  0.6689  0.8987  0.6025  0.9445  0.5544  0.0322  0.2246  0.0771   \n",
      "15    14.3049  0.4727  0.3363  0.6530  0.8783  0.9904  0.0898  0.0888  0.7062   \n",
      "16    11.6389  0.1599  0.5240  0.6362  0.1297  0.2799  0.3054  0.5711  0.7525   \n",
      "17    14.2806  0.7374  0.7694  0.1788  0.0701  0.1072  0.4532  0.5844  0.4427   \n",
      "18    16.9396  0.8132  0.9187  0.6541  0.0148  0.1239  0.2355  0.6451  0.5939   \n",
      "19    12.8732  0.4604  0.8288  0.3032  0.5593  0.0200  0.2189  0.1041  0.8064   \n",
      "20    15.0160  0.6141  0.5983  0.4313  0.4767  0.2693  0.5553  0.9060  0.0376   \n",
      "21    25.2078  0.9937  0.4566  0.1618  0.6551  0.2620  0.8422  0.8366  0.6788   \n",
      "22    11.0152  0.5214  0.5707  0.4965  0.2792  0.8285  0.0583  0.2948  0.3084   \n",
      "23    19.6342  0.3450  0.8200  0.4386  0.7942  0.3051  0.9454  0.0280  0.5356   \n",
      "24    17.0210  0.4771  0.8546  0.5754  0.0847  0.6028  0.6814  0.1998  0.8783   \n",
      "25    22.9183  0.7127  0.7003  0.5914  0.9064  0.1267  0.6628  0.8711  0.4959   \n",
      "26    12.1479  0.1042  0.3127  0.9906  0.7858  0.2306  0.1332  0.3042  0.6124   \n",
      "27    13.8648  0.8859  0.1135  0.2894  0.3308  0.0126  0.5328  0.7429  0.7122   \n",
      "28    12.4976  0.1422  0.2385  0.9046  0.5882  0.2057  0.3972  0.5173  0.3907   \n",
      "29     9.6890  0.1473  0.0727  0.4518  0.5175  0.7622  0.4373  0.4984  0.2078   \n",
      "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "4969  17.8709  0.7544  0.0705  0.7885  0.6195  0.9340  0.9902  0.2315  0.4988   \n",
      "4970  15.6963  0.2517  0.7828  0.2983  0.4855  0.2164  0.8591  0.7835  0.2339   \n",
      "4971  20.6620  0.7078  0.3308  0.3699  0.2858  0.8071  0.9840  0.1342  0.9395   \n",
      "4972  11.7536  0.2889  0.5618  0.2048  0.7623  0.4589  0.3932  0.9073  0.0742   \n",
      "4973  22.8553  0.9261  0.5350  0.8792  0.5439  0.1207  0.9576  0.0105  0.9242   \n",
      "4974   7.8416  0.4198  0.2005  0.3371  0.3486  0.1922  0.1183  0.1083  0.1618   \n",
      "4975  12.9495  0.1342  0.2736  0.2926  0.7935  0.1946  0.7123  0.3021  0.4173   \n",
      "4976  14.0682  0.9540  0.0173  0.0849  0.3166  0.1506  0.3462  0.9946  0.8171   \n",
      "4977   9.7189  0.3358  0.1887  0.9223  0.5151  0.8989  0.0139  0.4213  0.0991   \n",
      "4978  18.7975  0.5419  0.9509  0.7359  0.2736  0.2748  0.8840  0.4902  0.3720   \n",
      "4979  10.9884  0.1805  0.4911  0.7476  0.5394  0.6582  0.9530  0.0044  0.2193   \n",
      "4980  10.9129  0.2111  0.0615  0.4505  0.1013  0.7091  0.5235  0.9056  0.4990   \n",
      "4981  13.4816  0.5987  0.5978  0.3963  0.5615  0.3602  0.0469  0.2152  0.8524   \n",
      "4982  20.1609  0.9383  0.0703  0.6811  0.1435  0.2436  0.8746  0.9582  0.8341   \n",
      "4983  19.8519  0.7883  0.6530  0.5136  0.4031  0.5626  0.7084  0.1653  0.9993   \n",
      "4984  12.0696  0.4810  0.6885  0.8945  0.1320  0.2070  0.2810  0.1628  0.9126   \n",
      "4985  20.8500  0.9382  0.4354  0.7244  0.2043  0.9904  0.5301  0.5386  0.6633   \n",
      "4986  12.4770  0.5724  0.4034  0.7304  0.5721  0.1683  0.0303  0.5885  0.6735   \n",
      "4987   9.5198  0.7544  0.3119  0.5173  0.4998  0.2268  0.1030  0.2664  0.0730   \n",
      "4988  12.8937  0.3385  0.3030  0.9735  0.3817  0.3532  0.5169  0.4830  0.5528   \n",
      "4989  17.5558  0.9976  0.1859  0.7092  0.8361  0.8137  0.2382  0.7687  0.0060   \n",
      "4990  13.0081  0.6852  0.0316  0.2864  0.2993  0.2123  0.9147  0.3397  0.5575   \n",
      "4991  14.1025  0.5295  0.5499  0.4266  0.9644  0.8756  0.2227  0.4494  0.0171   \n",
      "4992  12.3320  0.9200  0.0560  0.4186  0.6423  0.3262  0.2672  0.0219  0.2630   \n",
      "4993  19.0565  0.8422  0.9877  0.3711  0.6208  0.3983  0.2288  0.7559  0.5726   \n",
      "4994  13.8878  0.7045  0.1568  0.9387  0.9822  0.2567  0.4157  0.2791  0.0831   \n",
      "4995   7.4685  0.5438  0.1537  0.0820  0.3999  0.2654  0.1641  0.4784  0.1179   \n",
      "4996  18.2477  0.3253  0.2932  0.7553  0.8625  0.4613  0.9772  0.7455  0.0087   \n",
      "4997  14.4877  0.6203  0.1153  0.8830  0.6597  0.1398  0.0531  0.6922  0.9912   \n",
      "4998   9.8801  0.5304  0.5087  0.3542  0.2898  0.2488  0.0202  0.6319  0.1404   \n",
      "\n",
      "          x8      x9  \n",
      "0     0.3364  0.6807  \n",
      "1     0.1429  0.0499  \n",
      "2     0.8921  0.9547  \n",
      "3     0.6069  0.9435  \n",
      "4     0.3261  0.5840  \n",
      "5     0.3341  0.2206  \n",
      "6     0.1686  0.9216  \n",
      "7     0.7188  0.3383  \n",
      "8     0.1097  0.0280  \n",
      "9     0.1099  0.4483  \n",
      "10    0.6894  0.8727  \n",
      "11    0.9002  0.2752  \n",
      "12    0.8264  0.3239  \n",
      "13    0.2710  0.9352  \n",
      "14    0.6104  0.4746  \n",
      "15    0.7641  0.1984  \n",
      "16    0.3634  0.6548  \n",
      "17    0.2325  0.9099  \n",
      "18    0.3025  0.9641  \n",
      "19    0.0623  0.4756  \n",
      "20    0.8383  0.1591  \n",
      "21    0.8896  0.7250  \n",
      "22    0.7895  0.4974  \n",
      "23    0.6406  0.9317  \n",
      "24    0.3020  0.7409  \n",
      "25    0.2879  0.4444  \n",
      "26    0.5311  0.5928  \n",
      "27    0.4507  0.0361  \n",
      "28    0.7394  0.7627  \n",
      "29    0.3901  0.7213  \n",
      "...      ...     ...  \n",
      "4969  0.2626  0.3878  \n",
      "4970  0.3777  0.5553  \n",
      "4971  0.8988  0.9639  \n",
      "4972  0.1329  0.1843  \n",
      "4973  0.5948  0.5312  \n",
      "4974  0.9555  0.9629  \n",
      "4975  0.8195  0.5979  \n",
      "4976  0.4726  0.0534  \n",
      "4977  0.4061  0.6759  \n",
      "4978  0.5082  0.5870  \n",
      "4979  0.1001  0.0388  \n",
      "4980  0.4481  0.5877  \n",
      "4981  0.6076  0.2089  \n",
      "4982  0.6868  0.3790  \n",
      "4983  0.7965  0.1464  \n",
      "4984  0.0840  0.1616  \n",
      "4985  0.7946  0.8476  \n",
      "4986  0.1189  0.1772  \n",
      "4987  0.8415  0.0482  \n",
      "4988  0.7499  0.0670  \n",
      "4989  0.2658  0.6113  \n",
      "4990  0.7646  0.2226  \n",
      "4991  0.3870  0.4825  \n",
      "4992  0.8350  0.8154  \n",
      "4993  0.3178  0.1369  \n",
      "4994  0.5673  0.1976  \n",
      "4995  0.2591  0.5959  \n",
      "4996  0.6279  0.7852  \n",
      "4997  0.0963  0.1609  \n",
      "4998  0.3462  0.8680  \n",
      "\n",
      "[4999 rows x 11 columns]\n",
      "            y      x0      x1      x2      x3      x4      x5      x6      x7  \\\n",
      "0      9.9631  0.8737  0.6502  0.3767  0.0882  0.4651  0.1083  0.0281  0.3784   \n",
      "1     11.8933  0.5529  0.9809  0.4754  0.3382  0.2906  0.4996  0.1573  0.2322   \n",
      "2     18.4781  0.5010  0.8614  0.5827  0.6587  0.5974  0.5807  0.9531  0.0095   \n",
      "3     15.6454  0.5277  0.3373  0.0907  0.9486  0.4858  0.5853  0.0874  0.3499   \n",
      "4      6.0095  0.0935  0.1500  0.5522  0.2237  0.9050  0.2684  0.1660  0.2327   \n",
      "5     18.6011  0.9101  0.9090  0.9697  0.0965  0.0664  0.6595  0.6491  0.2565   \n",
      "6      7.4015  0.4471  0.1969  0.0061  0.6999  0.1554  0.1944  0.5712  0.0360   \n",
      "7     13.2330  0.4562  0.2002  0.2464  0.5618  0.3560  0.6294  0.4177  0.5124   \n",
      "8     17.9489  0.7026  0.2793  0.0891  0.6346  0.9760  0.6770  0.5985  0.6257   \n",
      "9     19.3741  0.3582  0.9229  0.0313  0.7396  0.9455  0.7340  0.3100  0.5522   \n",
      "10    24.4776  0.5554  0.5515  0.2395  0.7754  0.6355  0.6501  0.9994  0.7211   \n",
      "11    12.6223  0.0242  0.8222  0.1830  0.3553  0.4483  0.5817  0.2263  0.5871   \n",
      "12    17.8194  0.8994  0.1646  0.9427  0.6424  0.8244  0.2875  0.4697  0.5090   \n",
      "13    18.4948  0.6437  0.1641  0.3104  0.8256  0.4939  0.6933  0.1529  0.8860   \n",
      "14    14.8303  0.0827  0.7929  0.2329  0.7534  0.7632  0.7759  0.4790  0.1630   \n",
      "15    12.6687  0.7018  0.7443  0.2067  0.5085  0.1351  0.2446  0.3851  0.4394   \n",
      "16    23.3501  0.6778  0.8515  0.1167  0.8145  0.2228  0.6292  0.6233  0.5580   \n",
      "17    18.2775  0.8433  0.4997  0.9713  0.2736  0.3606  0.6712  0.7803  0.2921   \n",
      "18    17.7318  0.5235  0.4628  0.4687  0.6864  0.9039  0.7805  0.2253  0.5712   \n",
      "19    10.7953  0.9754  0.2381  0.3154  0.0611  0.4693  0.2833  0.2056  0.4563   \n",
      "20    14.6289  0.7272  0.2177  0.6009  0.0935  0.0019  0.9932  0.5269  0.6616   \n",
      "21    20.5145  0.4560  0.0712  0.9962  0.8447  0.7418  0.8922  0.7308  0.4693   \n",
      "22    16.5789  0.9767  0.6023  0.4743  0.0575  0.3928  0.2143  0.3419  0.9137   \n",
      "23    13.4987  0.6159  0.0303  0.4041  0.6906  0.7830  0.8016  0.1198  0.0554   \n",
      "24    17.1760  0.9879  0.8109  0.0180  0.5417  0.0930  0.6697  0.0502  0.7974   \n",
      "25     9.9540  0.1960  0.3189  0.6164  0.2564  0.3174  0.2726  0.0676  0.8281   \n",
      "26     9.2586  0.2989  0.1183  0.0520  0.0116  0.9698  0.1224  0.3397  0.8755   \n",
      "27    13.0513  0.2176  0.1092  0.5891  0.8765  0.7844  0.6936  0.4132  0.2323   \n",
      "28    21.2464  0.8759  0.8252  0.8198  0.6577  0.5868  0.6417  0.3054  0.5227   \n",
      "29    22.0548  0.7320  0.9925  0.5219  0.5201  0.7931  0.2313  0.3783  0.8616   \n",
      "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "4970  16.2211  0.8116  0.6500  0.0262  0.6971  0.0775  0.1755  0.5846  0.5936   \n",
      "4971  19.6606  0.4126  0.1882  0.3032  0.9264  0.0232  0.8494  0.7368  0.6336   \n",
      "4972  13.9351  0.4065  0.1071  0.0863  0.8869  0.3553  0.3234  0.4226  0.7942   \n",
      "4973  13.5902  0.3726  0.1006  0.8080  0.9797  0.8611  0.4347  0.1357  0.1067   \n",
      "4974  16.1715  0.5642  0.6324  0.6497  0.6503  0.9261  0.6273  0.1708  0.0795   \n",
      "4975  14.8778  0.4800  0.4765  0.5359  0.8775  0.4471  0.1849  0.4574  0.3033   \n",
      "4976  15.8650  0.7680  0.1510  0.0609  0.8757  0.8343  0.1585  0.7407  0.5278   \n",
      "4977   8.5815  0.0460  0.0544  0.8633  0.4779  0.1691  0.6883  0.2013  0.2944   \n",
      "4978  20.9606  0.1275  0.6234  0.5142  0.7373  0.9106  0.6611  0.4880  0.8883   \n",
      "4979   9.0343  0.3538  0.2135  0.3184  0.2363  0.2346  0.3256  0.8393  0.1299   \n",
      "4980   9.9703  0.5633  0.3297  0.1195  0.9913  0.0374  0.3837  0.2019  0.0800   \n",
      "4981  17.5503  0.2961  0.4861  0.5465  0.2460  0.1981  0.9788  0.8978  0.3284   \n",
      "4982  14.2903  0.7143  0.6431  0.1072  0.0428  0.0045  0.8597  0.8249  0.1941   \n",
      "4983  17.1646  0.5989  0.3958  0.6413  0.2165  0.6633  0.7929  0.7938  0.2564   \n",
      "4984  10.2770  0.1005  0.8264  0.8223  0.0424  0.2709  0.2059  0.0588  0.8992   \n",
      "4985  19.0197  0.3466  0.7184  0.6054  0.5044  0.9940  0.8248  0.2057  0.7511   \n",
      "4986  10.1322  0.0301  0.0087  0.4108  0.9962  0.5659  0.8995  0.0122  0.1755   \n",
      "4987  14.9174  0.8394  0.4704  0.2717  0.4626  0.2853  0.4180  0.7017  0.3869   \n",
      "4988  20.4548  0.3456  0.3153  0.5847  0.8512  0.9585  0.9505  0.5289  0.6493   \n",
      "4989  12.2773  0.1150  0.5575  0.6960  0.1525  0.8656  0.2938  0.5174  0.7026   \n",
      "4990  14.7733  0.5120  0.6093  0.2568  0.1881  0.3499  0.4715  0.6986  0.7234   \n",
      "4991  12.8451  0.1906  0.9949  0.6609  0.0007  0.7645  0.7078  0.7882  0.1164   \n",
      "4992  12.5546  0.4006  0.3535  0.4618  0.4702  0.9016  0.3075  0.0462  0.6503   \n",
      "4993  14.3394  0.3723  0.9194  0.5901  0.2686  0.7339  0.0976  0.2640  0.8993   \n",
      "4994  14.4895  0.1868  0.5158  0.7238  0.1576  0.7534  0.4202  0.7386  0.7083   \n",
      "4995  13.2491  0.0032  0.1474  0.5509  0.7817  0.7053  0.5884  0.8389  0.0514   \n",
      "4996  17.2065  0.6932  0.2778  0.9967  0.5884  0.0650  0.7935  0.1715  0.4824   \n",
      "4997   8.8285  0.2096  0.7559  0.3008  0.1919  0.2689  0.3101  0.5241  0.0980   \n",
      "4998  18.7595  0.9414  0.9118  0.5250  0.2994  0.1601  0.1368  0.8911  0.4093   \n",
      "4999  25.9478  0.8614  0.7071  0.9490  0.7746  0.4524  0.8917  0.5429  0.4171   \n",
      "\n",
      "          x8      x9  \n",
      "0     0.7557  0.1379  \n",
      "1     0.1774  0.1820  \n",
      "2     0.7809  0.2032  \n",
      "3     0.8960  0.8480  \n",
      "4     0.0737  0.5217  \n",
      "5     0.3284  0.6048  \n",
      "6     0.5602  0.0068  \n",
      "7     0.9280  0.2980  \n",
      "8     0.6400  0.3510  \n",
      "9     0.8600  0.5100  \n",
      "10    0.9850  0.7614  \n",
      "11    0.9610  0.5282  \n",
      "12    0.2283  0.7484  \n",
      "13    0.8357  0.5478  \n",
      "14    0.4180  0.5290  \n",
      "15    0.5013  0.1227  \n",
      "16    0.9135  0.8843  \n",
      "17    0.6451  0.2085  \n",
      "18    0.4411  0.4885  \n",
      "19    0.3814  0.6333  \n",
      "20    0.2412  0.2899  \n",
      "21    0.4089  0.6467  \n",
      "22    0.7864  0.6171  \n",
      "23    0.9171  0.6621  \n",
      "24    0.2963  0.0942  \n",
      "25    0.3509  0.7592  \n",
      "26    0.3941  0.9869  \n",
      "27    0.4570  0.3788  \n",
      "28    0.5411  0.1217  \n",
      "29    0.4339  0.9193  \n",
      "...      ...     ...  \n",
      "4970  0.0938  0.7737  \n",
      "4971  0.5012  0.9493  \n",
      "4972  0.5447  0.5719  \n",
      "4973  0.6829  0.8306  \n",
      "4974  0.4669  0.8039  \n",
      "4975  0.9011  0.5095  \n",
      "4976  0.3386  0.5938  \n",
      "4977  0.7440  0.1752  \n",
      "4978  0.6009  0.9810  \n",
      "4979  0.9896  0.1984  \n",
      "4980  0.3210  0.1537  \n",
      "4981  0.9866  0.8906  \n",
      "4982  0.2736  0.6930  \n",
      "4983  0.9511  0.5684  \n",
      "4984  0.9951  0.0291  \n",
      "4985  0.2480  0.7009  \n",
      "4986  0.0956  0.5169  \n",
      "4987  0.6353  0.1619  \n",
      "4988  0.9485  0.1781  \n",
      "4989  0.2259  0.7397  \n",
      "4990  0.7138  0.3432  \n",
      "4991  0.4470  0.1413  \n",
      "4992  0.7313  0.6113  \n",
      "4993  0.3586  0.4754  \n",
      "4994  0.5044  0.6899  \n",
      "4995  0.8259  0.7905  \n",
      "4996  0.3038  0.8592  \n",
      "4997  0.4449  0.5373  \n",
      "4998  0.5835  0.7275  \n",
      "4999  0.4776  0.7074  \n",
      "\n",
      "[5000 rows x 11 columns]\n",
      "train data shapes  (4999, 10) (4999,)\n",
      "test data shapes  (5000, 10) (5000,)\n"
     ]
    }
   ],
   "source": [
    "#Load data\n",
    "import os\n",
    "import gc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import HTML\n",
    "\n",
    "path=\"\"\n",
    "\n",
    "train=pd.read_csv(path + \"train.csv\")\n",
    "print (train)\n",
    "\n",
    "test=pd.read_csv(path + \"test.csv\")\n",
    "print (test)\n",
    "\n",
    "y=train[\"y\"].values\n",
    "y_test=test[\"y\"].values\n",
    "\n",
    "train.drop(\"y\",inplace=True, axis=1)\n",
    "test.drop(\"y\",inplace=True, axis=1)\n",
    "\n",
    "X=train.values\n",
    "X_test=test.values\n",
    "\n",
    "print (\"train data shapes \", X.shape, y.shape)\n",
    "print (\"test data shapes \", X_test.shape, y_test.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WRa9VCGEEba0ALokxVkqEXdjOSqQWKtW6GFiUPf5imXkKF4z/IV3gFmwEPEPKbXRch4vy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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169c822e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " mean of train  15.318999259851973\n",
      " mean of test  15.619698139999999\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = [20,10]\n",
    "\n",
    "plt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"proportion\", fontsize=30);\n",
    "plt.xlabel(\"target values\", fontsize=30);\n",
    "plt.title(\" Distribution of target (y) values for train and test \", fontsize=30)\n",
    "plt.legend( prop={'size': 30})\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print (\" mean of train \", np.mean(y))\n",
    "print (\" mean of test \", np.mean(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================== Start of Level 0 ======================\n",
      "Input Dimensionality 10 at Level 0 \n",
      "1 models included in Level 0 \n",
      "Fold 1/4 , model 0 , rmse===0.586376 \n",
      "=========== end of fold 1 in level 0 ===========\n",
      "Fold 2/4 , model 0 , rmse===0.636539 \n",
      "=========== end of fold 2 in level 0 ===========\n",
      "Fold 3/4 , model 0 , rmse===0.599659 \n",
      "=========== end of fold 3 in level 0 ===========\n",
      "Fold 4/4 , model 0 , rmse===0.615107 \n",
      "=========== end of fold 4 in level 0 ===========\n",
      "Output dimensionality of level 0 is 1 \n",
      "====================== End of Level 0 ======================\n",
      " level 0 lasted 0.011966 seconds \n",
      "====================== Start of Level 1 ======================\n",
      "Input Dimensionality 1 at Level 1 \n",
      "1 models included in Level 1 \n",
      "Fold 1/4 , model 0 , rmse===0.579685 \n",
      "=========== end of fold 1 in level 1 ===========\n",
      "Fold 2/4 , model 0 , rmse===0.641933 \n",
      "=========== end of fold 2 in level 1 ===========\n",
      "Fold 3/4 , model 0 , rmse===0.587644 \n",
      "=========== end of fold 3 in level 1 ===========\n",
      "Fold 4/4 , model 0 , rmse===0.613380 \n",
      "=========== end of fold 4 in level 1 ===========\n",
      "Output dimensionality of level 1 is 1 \n",
      "====================== End of Level 1 ======================\n",
      " level 1 lasted 0.008001 seconds \n",
      "====================== End of fit ======================\n",
      " fit() lasted 0.019966 seconds \n",
      "====================== Start of Level 0 ======================\n",
      "1 estimators included in Level 0 \n",
      "====================== Start of Level 1 ======================\n",
      "1 estimators included in Level 1 \n",
      "rmse on test is 0.632957 \n",
      "correlation on test is 0.995326 \n"
     ]
    },
    {
     "data": {
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rlFKDMOO4MNAC43LlFIBXBNi1S3HHKMuPcxnuZ1ige9o8a30ljKg4Uyn1OeZ+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0u03WzIen46MqMfPPxbxeKL94/au7tL3Ts2q72lUW6X1N7SqO4dm7V3d1dBJXcAAABALSjFCidJ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\n5PN6FAp2zunhVC4vO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e46b9ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "all_preds=[]\n",
    "all_names=[]\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=1\n",
    "\n",
    "models=[\n",
    "    #1ST level #\n",
    "    \n",
    "    [Ridge(alpha=0.1, normalize=True, random_state=1234)],\n",
    "    \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.1, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds=model.predict(X_test)\n",
    "\n",
    "print (\"rmse on test is %f \" %(np.sqrt(mean_squared_error(y_test,preds))))\n",
    "print (\"correlation on test is %f \" %(pearsonr(y_test.reshape(-1,1),preds)[0]))\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds)[0],np.sqrt(mean_squared_error(y_test,preds)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30);\n",
    "plt.xlabel(\"Test target\", fontsize=30);\n",
    "plt.title(\"Scatter plot of [R(idge)][R(idge)] StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds)))\n",
    "all_names.append(\"[R(idge)][R(idge)]\")\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.plot(figsize=(1000,100))\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G/Iq9DVMAPB6TbWWbn4AZPexHzIg/yZgbp6uclknVWtsyh76w9h/v9Bq+B+7Q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WWxgdN/Y3qoVANcUvolSVolklye9332G4yM5pd97IvbG3jzT15LW3P9WaGSq5gkSZKk1WmxgRMU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\nTMrUptna2rCo+0uSJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e483fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    #1ST level #\n",
    "    \n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234)],\n",
    "    \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds1=model.predict(X_test)\n",
    "\n",
    "#print (\"rmse on test is %f \" %(np.sqrt(mean_squared_error(y_test,preds1))))\n",
    "#print (\"correlation on test is %f \" %(pearsonr(y_test.reshape(-1,1),preds1)[0]))\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds1,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds1)[0],np.sqrt(mean_squared_error(y_test,preds1)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30);\n",
    "plt.xlabel(\"Test target\", fontsize=30);\n",
    "plt.title(\"Scatter plot of [R_opt][R_opt] StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds1)))\n",
    "all_names.append(\"[R_opt][R_opt]\")\n",
    "\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================== Start of Level 0 ======================\n",
      "Input Dimensionality 10 at Level 0 \n",
      "2 models included in Level 0 \n",
      "Fold 1/4 , model 0 , rmse===0.428845 \n",
      "Fold 1/4 , model 1 , rmse===0.787817 \n",
      "=========== end of fold 1 in level 0 ===========\n",
      "Fold 2/4 , model 0 , rmse===0.448941 \n",
      "Fold 2/4 , model 1 , rmse===0.761795 \n",
      "=========== end of fold 2 in level 0 ===========\n",
      "Fold 3/4 , model 0 , rmse===0.465667 \n",
      "Fold 3/4 , model 1 , rmse===0.800146 \n",
      "=========== end of fold 3 in level 0 ===========\n",
      "Fold 4/4 , model 0 , rmse===0.451505 \n",
      "Fold 4/4 , model 1 , rmse===0.747920 \n",
      "=========== end of fold 4 in level 0 ===========\n",
      "Output dimensionality of level 0 is 2 \n",
      "====================== End of Level 0 ======================\n",
      " level 0 lasted 1.764710 seconds \n",
      "====================== Start of Level 1 ======================\n",
      "Input Dimensionality 2 at Level 1 \n",
      "1 models included in Level 1 \n",
      "Fold 1/4 , model 0 , rmse===0.383720 \n",
      "=========== end of fold 1 in level 1 ===========\n",
      "Fold 2/4 , model 0 , rmse===0.403476 \n",
      "=========== end of fold 2 in level 1 ===========\n",
      "Fold 3/4 , model 0 , rmse===0.410379 \n",
      "=========== end of fold 3 in level 1 ===========\n",
      "Fold 4/4 , model 0 , rmse===0.404465 \n",
      "=========== end of fold 4 in level 1 ===========\n",
      "Output dimensionality of level 1 is 1 \n",
      "====================== End of Level 1 ======================\n",
      " level 1 lasted 0.007968 seconds \n",
      "====================== End of fit ======================\n",
      " fit() lasted 1.772678 seconds \n",
      "====================== Start of Level 0 ======================\n",
      "1 estimators included in Level 0 \n",
      "====================== Start of Level 1 ======================\n",
      "1 estimators included in Level 1 \n"
     ]
    },
    {
     "data": {
      "image/png": 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Ypqh8UZyOY6lwjPQohBBCiGKSLnVCCCGEEH4kwSYhhBBCiHNklLr4+JTyv5On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qUMDCf0zTG1bLVrPb2OLZ0d3P/dIyWnx83X9DudSrFreyc7tl7AwHBCa1PGUU2SJEnSIiwpcIox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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e4869240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=1\n",
    "\n",
    "models=[\n",
    "    #1ST level #\n",
    "    \n",
    "    [\n",
    "        Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.06,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1)],    \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds2=model.predict(X_test)\n",
    "\n",
    "#print (\"rmse on test is %f \" %(np.sqrt(mean_squared_error(y_test,preds2))))\n",
    "#print (\"correlation on test is %f \" %(pearsonr(y_test.reshape(-1,1),preds2)[0]))\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds2,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds2)[0],np.sqrt(mean_squared_error(y_test,preds2)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30);\n",
    "plt.xlabel(\"Test target\", fontsize=30);\n",
    "plt.title(\"Scatter plot of [R,GBM][R] StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds2)))\n",
    "all_names.append(\" [R,GBM][R]\")\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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09nQ8+GARjuFBJ3BkX4hiZAU4nYOTA5VSbTy1L8J+dmutb8ZkB40B/sQMtUvC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lAAAg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\nhpJMpfTJe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NbnxwbFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e56cf198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    #1ST level #\n",
    "    \n",
    "    [\n",
    "    Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 )],     \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds3=model.predict(X_test)\n",
    "\n",
    "#print (\"rmse on test is %f \" %(np.sqrt(mean_squared_error(y_test,preds3))))\n",
    "#print (\"correlation on test is %f \" %(pearsonr(y_test.reshape(-1,1),preds3)[0]))\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds3,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds3)[0],np.sqrt(mean_squared_error(y_test,preds3)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30);\n",
    "plt.xlabel(\"Test target\", fontsize=30);\n",
    "plt.title(\"Scatter plot of [R,GBM,ET][R] StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds3)))\n",
    "all_names.append(\"  [R,GBM,ET][R] \")\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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bO8rcrc7rCUC/YOvz8x4SRGQB9nPe2Hn5q8zWthKRR4DJztPPjDGPBJn94gyy\nGvw54nRlzHYisga4xnl6rTFmTbD5vRlj/hWRv7ABYtf+O5PssIIi8nqI8y4yxmRY4+9CYoxZISKT\nsYNRALwvIldksgtbRts45nQ3W4Y9jz4EtBOR2dhA615spvDFwE3OdNfndnCA79H92CzB60XkIWwX\nuMNO8HcmnkL8453vl8XYLno1gQeAdmnW53MeNcbsFpHnsN/JNYF1zvf6Sux3QTPsuaMw9mbBk5nY\nJf2ANs6+eFZEZjldHDPN+TwMw9bMCzbfAhGZiv3+qgtsEJHx2OzscGyX8mfw1BGc6mckPe/zXB8R\n2Y7NevrD6RqulFKZogEnpVSeYYxJFpH7sYGnZ7E/Im/Fc2HpzzTgaX9dt4wxf4nIbdjhp8thf4z6\n+0H6Dl7ZSsaY+SIyEZvJUQD7AzBtWjvYjKjS2B/VNfwUVJ2DHVoabHHS0djUe9doen8Ae7A/2Jvj\nSbeviefC/ynsUPD9nG0N99MO17a6BJiW3eZigy434NulyeVTPF0WQmKMOSIiNzvrbojtxuEvw+0Q\n0NW5Q53WQuzFTDHsEO33AadFpFioP8SNMW+KiOtufnFsF0V/3RT/D3jiAsluAsAYs8YJYPZ2XnpL\nRH4Msdtj2nVNEZHV2OygNtiL1P4ZLLYN+1mbEMLfY12ITXEdD1kpwDsLT2AZcr473ePOv8z4DDvC\n1/loFp76eCnA7DNYVwFs4CAU8YQwqMQFaDD2XFIWG3h/Cd+usGfMOSc2xWbQtMAGlHriyVpMKxoY\naIyZEmD6HGxdOFd2K9is4d7AdKAt9jNWCHtOf9bPOj7CnvNaA7VFpIB3HTXnOzkCGIEdrMFfUCcO\neNgY86ufaX4ZY6JEZDCeAUImiUiTLA6oAPa78D7sjaBgumPrMz6OHWAj0Oh2/4e9aZbWP9jaiHWx\nAWNXd77G2N8USimVKdqlTimVpxhjEo0xw7EZPU/iyW6KxqbWH8RejL6OvSv/oDHmcJD1LcfWkngB\nOxrQMWc9e7EXmDcZY5520uK9l3scW6dnobPtZOyP2n+Az4EbjTGPAgucRQqQpu6PMeYb7LDP67E/\nMOOwP7xd009hR/+Zj607kYgNQFX1mifZGOPKHPrI2X6c13v4CrjDGHOPs76zIQr7o3oEsBV7AbgL\ne/FxizGmR1YKLzuZHNdi7/5+g72TmwgcwN4BfhqQAMEmjDH7sftzMRCDzXzZR/CitP7WMx4QbJfA\nTdiRCk/3rWVxAAAgAElEQVRij8MpQDNjTBdjTEhFqs8zL2D3J9jMuIyG8g7IGLPFGHM7NlvwZWyW\nxH7sfo/HZjqtwNYXuxWo7RQOzupd+BTssb8De3z0AWoFOh5C8CP2bwv2mPZXo0YF5l2vabkx5kDA\nOVWGjDHR+AZkBogdMTW7t7PFGHMrNotpLDYLeCc28/cU9mbHj9ibBrWDBJvAdn9+HpuJmoA9vxZ3\ntpNqjOmC/T5fgs1Qdp1HNwOTgOuMMb2A75z1FcUGqdK2+VWgHjaYtRX7fXwKez4cD1xhjJmT+b3B\nJ3hqP15NFgdUcNqYjM1QC3ruM8acNsY8gT3XfYJ9Pyew57rd2N8lrZxzTLrvSmNMCnAbNoPskLO9\nfXgynZVSKlPCUlOzo8yBUkoplXVpultNcH4wqxwgIlOwgUqAimf7Qt7palbDGJPtF7u5lTN8+xRj\nzFMZznweEZGHsUHUOsYYc46bc95wupm5ij8XdwWXRaQYniDlV8aYjme5XX2wXVgB2js3NZRSSqks\n0wwnpZRSSp1NV2CziFQIRKQ6thvnhbjP6mEzJLTYt1JKKZUHacBJKaWUUmeFiAwELsEzaqAKQkQK\nYQsaJ3BmNYzOOqeA+WPAXGfIdqWUUkrlMVo0XCmllMq7rvAaOtvkZJ0uZzsvAuONMTNzaju5TAvs\nkO+PGGN2n+vGZNJoYDuZLPCfW4lIXSDCeepv1NG0SnqNOhhnjNmWQ+2qii0kDlA5J7ahlFIq79KA\nk1JKKZV3eQ+J3RBbgD5HGGMOiMilwYrwK1/GmO9EpPoFus8eBI5fSCMt5rAF2MEqQtUSz2iKK4Bm\n2d4iaxh2ZDOllFIq22nASSmllFJnxQUaODmnLtR9Zow5eq7boJRSSqlzK0+MUnfoUGzuf5NnSenS\nRTh69OS5boZSZ0SPY3Wh02NY5QZ6HKvcQI9jlRvocazORPnyxcMCTdOi4SpT8ucPP9dNUOqM6XGs\nLnR6DKvcQI9jlRvocaxyAz2OVU7RgJNSSimllFJKKaWUylYacFJKKaWUUkoppZRS2UoDTkoppZRS\nSimllFIqW2nASSmllFJKKaWUUkplKw04KaWUUkoppZRSSqlspQEnpZRSSimllFJKKZWtNOCklFJK\nKaWUUkoppbKVBpyUUkoppZRSSimlVLbSgJNSSimllFJKKaWUylYacFJKKaWUUkoppZRS2UoDTkop\npZRSSimllFIqW2nASSmllFJKKaWUUkplKw04KaWUUkoppZRSSqlspQEnpZRSSimllFJKKZWtNOCk\nlFJKKaWUUkoppbKVBpyUUkoppZRSSimlVLbSgJNSSimllFJKKaWUylYacFJKKaWUUkoppZRS2UoD\nTkoppZRSSimllFIqW2nASSmllFJKKaWUUuosSTidzMGjJ0k4nXyum5Kj8p/rBiillFJKKaWUUkrl\ndskpKcxYso11Ww9x5HgCZUpE0LB2ee5vcRnh+XJfPpAGnJRSSimllFJKKaVy2Iwl21i0Zo/7efTx\nBPfzzi1rn6tm5ZjcF0JTSimllFJKKaWUOo8knE5m3dZDfqet23o4V3av04CTUkoppZRSSimlVA6K\niUvgyPEEv9OOxsYTE+d/2oVMA05KKaWUUkoppZRSOSThdDKJSSmULl7Q7/TSxQtRsljEWW5VztMa\nTuqCMGrUy3z//TcZzhceHk6RIkWpUKECInVp1+5O6tdvcBZaCElJScybN4dFi35gx47tnD6dRPny\n5bn22ibce28nqlevccbbOHIkmhkzprFy5a/s37+PlJQUqlSpStOmN3LvvQ9QpkzZDNexbt1a5s6d\nzYYNf3H06BGKFCmKSB3atLmDVq3akC+DYnXJycksXvwTP/zwLVu3GuLiYilatCi1agmtW99O69a3\nkz9/8K+WI0eimTXrS1auXMG+fXtJSjpN2bLlaNDgajp2fACROpnaL+r8sHHjX8ycOZ2NG//i2LGj\nlCxZkksvrU27dnfSokXLM15/UlISixb9yOLFP7F1qyEm5hgRERFUqlSZJk2a0rHjA5QrVy7oOg4f\nPsScObP4/fdV7Nmzm/j4U5QoUZJatYSWLVvTqlWbDI/f5cuX8c038/jnn80cPx5D6dJluOyyWrRp\ncwctWrQiLCws6PLx8fF8++18li9fxvbt/xIXF0vhwkWoVq06TZs24+6776N48eKZ3j9KKaWUUur8\nkrZIeETBcL/zNaxdjogC/qddyMJSU1PPdRty3KFDsbn/TZ4l5csX59Ch2LO+3VADTv507Hg//foN\nyuYW+YqJOcbAgX3ZsmWz3+kFC0YwaNAQbr+9XZa38euvy3nllRc5efKE3+lFixbllVfG0KTJ9X6n\nJyUl8cYbY1mw4OuA27jyyvqMGfMmJUuW8jv95MkTDBkykLVr/wi4jvr1GzBmzBuUKFHS7/R169Yy\ndOhgYmJi/E7Ply8fjz3Wi65dHw24jTN1ro7j3GzSpIlMnvwxgc4pN97YnOHDR1OwoP+7OhmJijrA\n888PwpgtAecpXLgIQ4e+zM03t/A7ffHihYwZM4JTp04GXEfdupczevTrlC9fId20hIQEXn75BX75\nZVnA5Rs0uJoRI8ZQunQZv9O3b9/G888PZO/ePX6nA5QuXYaRI8dx1VWBg+V6DKvcQI9jlRvocaxy\nAz2Oc860RVt9ioS7FCoYTuLpZEoXL0TD2uUu6FHqypcvHvBuqwacVKacDwGnZ58dSp06df3Ol5h4\nmqioA6xY8TM//fSD++K3b98B3HdfpxxpW0pKCn37PsH69X8CcMstLWnbtj3FihVjw4b1TJ06mbi4\nOMLDwxk//n2uvrpRprfx559r6N+/N8nJtpDcjTfeTNu27SlTphz//bed6dOnsnNnJOHh4YwcOZYb\nb2yebh1jxozgm2/mAfbC/P77O9OoUWNSU1NZvXols2ZNJyEhgapVqzFx4md+MyyGDBnAL7/8DED1\n6jXo3Pkhqlatxv79+5g5c7o7GNCgwdW8++6EdJkeBw7s55FHOhEXFwfAbbfdTvPmt1K0aDH+/nsj\nX3wxhRMnbEDtuedepF27OzO9r0KhJ9XstWDBXMaOHQlAlSpV6dr1UWrUqMmBA/uZMeP/2Lx5EwB3\n3PE/hgx5KdPrT0iIp3v3h4iM3AFAo0aNadfuTipVqsyJE3H89tsK5s6dzenTpwN+ztas+Z0BA54i\nOTmZggUjuOuujlx33fUULVqMffv2MnfuV+7P8KWX1mLChMkUKlTIZx3Dhj3P4sU/AVCjxiU88EAX\nqlWrzqFDh/j223n8/vsqAK688irefXdCukyp6OjDdOv2INHR0YD9rmjZ8jYqVKjA0aNHWbZsMd9/\n/w2pqakUKVKUiROnUKPGJX73iR7DKjfQ41jlBnocq9xAj+OckXA6maEfryLaT92msiUieLpjfcqX\nLnLBZzZpwEkDTtnmfAg4vfPORyEFbZYuXcRLLw0hNTWVUqVK8dVX3xIRkf39Yr/9dj6vvvoKAJ06\ndaV376d9pu/cGUmvXt05fjyGmjUvZcqU6Rl2W/OWlJREp053s3//PgCefPJpOnfu6jNPfHw8Awf2\nZf36PylbthzTp39FkSJF3dP/+GM1/fv3Bmz2xDvvfMQll9T0WceWLX/z1FOPEx8fz91338szzzzr\nM33jxr/o1as7ALVr1+Gjjyb5ZKskJyczaNDT7ovuUaPGpcs0GTt2lDvDyt/72Lkzku7duxAfH0/J\nkiWZO/cHChQoEPK+CpWeVLPP8eMx3HdfB+LiYqlSpRoTJ06hRIkS7ulJSUkMHTqYX39dDsDEiVO4\n/PJ6mdrG1KlTmDDhPQDuv78zTz31TLp51q1byzPP9OH06dNUr16DqVNnuj9nqampdOlyLzt3RlKw\nYATvvPMR9epd6bN8amoqb7wxhrlzvwKgZ88neeihbu7pf/65hr59nwCgXr36vPvuhHTHpvfx/dJL\nI2nduo3P9NdeG828eXOAwEHwhQt/YPjwoQBce20Txo9/3+8+0WNY5QZ6HKvcQI9jlRvocZxewulk\nYuISKFksIssBoYNHTzJkwir8BSPyhcHontdRoXSRM2voeSBYwOnCzNlSKgS33NKSZs1uAuDYsWNB\nu4GdiRkz/g+AMmXK0qPH4+mmV69eg27dHgNgx47trFr1W6bWv2LFcnew6cYbb04XpAEoVKgQL774\nCvnz5yc6+jBffvl/PtNnz/7S/XjQoOfTBZsA6ta9gkce6QHAvHlz0nX58W73Y4/1Stc1Kjw8nD59\n+nm1+5d021i92q6jbNmydOrUJd306tVr0KFDRwBiYmLYtGlDunnU+eXbbxcQF2d/oPTq1ccn2ASQ\nP39+Bg9+wZ0tNG3a1CxsYz4A5ctXoFevvn7nadjwGu68827ABi43b/7bPW3Tpg3s3BkJ2C62aYNN\nAGFhYTz11DPurnA//PCtz3RXdiDYz5C/QGivXn3cj5cuXegzLSEhgYULfwTsZy1QxmWrVm244YYb\nAZuVdfiw/6FzlVJKKaVU9juZcJpPvtnMCxNXMmTCKoZ+vIppi7aSnJLinifhdDIHj54k4XRy0HWV\nLBZBmRL+Ex5ya5HwtDTgpHK1a6651v14z57d2b7+3bt3sWPHdgCaN29BREQhv/O1bdue8HAbGV+6\ndFGmtuEdKLv33sDdAi+66GIaNWoMwJIlnovd1NRU1q2zXYUqVqzETTc1D7iOtm3bAzZbadmyxT7T\njh494n5crVp1v8tXr36JO6vk8OHD6aa71lG5ctWAhZVr1rzU/Tg6Ov061Pll+fIlABQrVoxmzW72\nO0+ZMmW5/vpmAKxatYL4+PiQ13/kSDR79uwC4LrrmgYt6N2oURP3423btrof//XXOvdjVxDan4iI\nCOrXvwqAXbt2kpiY6J5WqVJlrrzyKmrVqs2ll17md/kSJUq6A1ZRUQd8pm3b9q+7/lqwNoDNbAL7\n2d2+fVvQeZVSSiml1JlLTklh2qKtDHz/N37bdIAjsYmkAtHHE1i0Zg8zlmxzzzP041UBg1HeIgqE\n07B2eb/TcmuR8LR0lDqVq6V4ffiTkk77TOvTp6e7ZktmPP/8MHdgZuPGv9yvN2x4TcBlihQpymWX\n1caYLZnOtDpwwHPhesUVwbsi1ahRk1WrfmPnzkhiY2MpXrw4x4/HuC9069a9IujyZcqUpWTJkk52\n0UafaeXKeb4sd+6MpHLlKumWd42cZ+dPP1pYuXLl2b9/H7t37yI1NdVv0Mk7MOi9zezSsWN7DhzY\nz733dqJr10cYP/41Vq9eSWpqKhUrVqRLl0dp3bqN+/ho3rwFI0eOY8OG9cycOY2NGzcQGxtL2bLl\nuOGGZnTp8qj7ve7du4fp06eyevVKDh8+RNGixahfvwEPPfQodepc7rc9x48fZ+7c2fz2269ERu4g\nPj6e4sVLUL16Da67ril33nlP0BHLUlNTWbJkIQsX/sA//2whJuYYRYoUoXr1S2jW7GY6dLiHIkXS\np+p+990CRo8enun916DB1bz33kTAdpdzFcqvX7+BO6jqf7mGLF26iPj4eP7+e6NPMDiYsLB89Ojx\nBIcPHw76GbM8CcvewaLLL69H166PcvjwIapUqRp8DV45z4mJie5Mvh49nqBHjyeCLnviRByxsccB\nKFvW9/gvVaoU3br15PDhQ1x55VWZaEP6Pv9KKaWUUip7zViyzW9xb5d1Ww+TnJLK0j/3ul9zBaMA\nOres7Xe5+1tc5l7+aGy8T5HwvEADTipXW7/ek9lQrVqNbF9/ZOR/7sdVqlQLOm/lylUwZgsHD0Zx\n6tQpChcuHNI2XIGy8PDwgBlULq7sj9TUVPbs2UXduldw+nSSe7q/wEOgdezevcvn9RtuuIlJk2yg\nYdKkiTRufJ1PtklqaioTJnjqzdxyS8t0677hhpuYPftLjh49wsyZ07j//gd9ph88GMXXX88GoEKF\ni7jiivRdn7LLiRNx9O79mM/73LFjO+XLpw9yff75JD7++EOfEdj279/L7NkzWL58GRMmTGbrVsPw\n4UN9RhE8duwoy5cvZeXKXxkz5s10Iwhu2/YvAwY8lS6T6+jRIxw9eoT16/9k2rSpjBs3nnr16qdr\n19GjR3j++UE+gU+w3RE3bFjvDpKNHDnW7/Jnas+e3SQl2eMro0BOpUqeAGVk5H8hB5xKly7t7uqZ\nkXXr1rofX3xxRffjq69uFFLdt6SkJPe+LFasGMWKFQtpuy6TJk10748WLVr5TKtcuQrduvUMaT2B\n3odSSimllMo+rjpNhSPys25r8DIGR47Hs36r/94X67Ye5p6bL/WbsRSeLx+dW9bmnpsvPeOaUBci\nDTipXOuPP1azYoUtVFyqVCl3dzOX5557Mejw6IFcdNHF7sfe9VW8X/enQoWL3I8PHToYsFtaWiVL\nlgJsN7fo6MPpMie8HTwY5X7sGgmrRIkShIWFkZqaysGDB4NuKyEhnmPHjgG2K5M3kTrcf39nZsyY\nxj//bKZbtwd54IEuVK1azR0ocmWMtWt3J9dff0O69T/8cDd+/30lu3bt5L333mL79m3cdFNzihcv\nwT//bGbq1MnExh6nYMEIhgx5MUcKhrv88MO3pKSk0K7dnbRpcwdxcXGsWbM6XRbN+vV/smzZEsqX\nr0CnTl2pU6cu0dGH+fzzSfz771YOHozilVdeZPPmTRQsGEHPnk/SoMHVJCYm8u2381m48AdOnz7N\nG2+M4csvv3Z3OUxOTmbo0GeJjj5M4cKF6dSpK1dd1ZAiRYoQHX2YJUsW8dNP33P8eAwvvvgcX345\nxyfgeOrUKZ566gkiI3cQFhZG69ZtuPnmWylfvjwxMTGsWrWC+fPncvjwIfr378OECZN9uis2a3YT\nkyf71voKReHCnqDloUOe4ymj4/+iizzHf07UJTp69Ii71lP+/PmzNBrkN9/Mc3f7bNz4+gzmthmU\nR44cwZgtzJw5zZ29eP31N9CqVZsMlvbvv/92uL+3ypYty2WX+b9bppRSSimlsiY5JYUZS7axbush\njhxPoFSxCI7GBc8qL1msIMcCzHM0Np6YuISgBcAjCoTnigLhmaUBJ5VrJCcnc+JEHHv27Gb58mXM\nnDmN5GRbyK13737phjjPKCMjFMePx7gfZ5Q95J3R5CqyHIrLL6/HwoU/ALB8+TLuuquj3/kSExPd\nI8QBxMefAqBgwYLUqlWbrVsNGzasIybmmDuIldaqVSvd+8y1vLennnqGmjUvY9KkiezYsT1dl6xS\npUrx5JNPc/vt7fyuv3TpMnzwwadMnjyRefPm8N13C/juuwU+81x9dSP69h3AZZfV8ruO7JKSkkKr\nVm147rkX3a/5q61z7NgxypUrz8SJUyhfvoJPO++++w4SEhJYt24txYoVZ8KEyT6BxEaNGnP6dCLL\nli1h3769bN++jVq1bABhw4b17tpEgwY9T+vWt/tst1mzmylXrhzTpk3l0KGDrFy5gubNb3VPnzjx\nAyIjdxAeHs7o0a+7C027XHddU9q0uYM+fXpy6tRJxowZwcSJU9zTS5QoSYkSJbOw5zyOHz/ufuw9\nKqI/hQp5jv/Y2OwdBSU1NZUxY0YQFxcHQLt2HTKdnbRnz24++ug99/MHHngwyNxW//59WLv2d/fz\nfPny0bnzQzz66GNBa00FkpCQwMiRw9yfwfvu6xyw1plSSimllAqd96hzX/283af7XEbBJoCGtcqx\nYXs00cfTz5tXCoBnhQaccoHsGLLxQuIamjwUERER9OnTP2AA5EydPu3p7pbRBWbBgp4vIddyobjl\nlpZ8+OE7JCYm8umnE2jS5HoqVaqcbr5PPvmQY8eOup+7uvYA3HZbW7ZuNcTHx/PGG2N5+eVR7kwb\nl9jYWD788F2/y7scPnyIzZs3ERNzzG9bjx07xtKli6hT53KfbBpvW7f+w/bt2/yuH+Cff7awePFP\nVK1ajYiInP3ido2Il5EuXR72CTaBzTxr2PAa9+h99977gN+stWbNbmbZMltYe+/e3e6Ak3cGWaDg\n5733diI2No5KlSpTubJnntjYWBYs+BqA9u3vShdscqlT53I6d36ISZMmsnnzJv7+e1OGdcAy4/Rp\nT52ktKMWpuX9t/ReLju8++6b7lERy5UrT/fu6UeLDObo0SMMHtzPHQhu374Dl1+e8X6Kitrv8zwl\nJYVff11OlSpVadfuzky1ITk5mREjXsSYLQBceulldOz4QKbWoZRSSimlfKXNZipdvCAnE4KPLuet\nUMFwmtWvyP0tLiM83H+dp7xSADwrNOB0AUv74SlTIoKGtcvbD0O+vDsAYcGCBbn00lpcd11T2rfv\n4NOVLbulDdqELvSshXLlytGlyyNMmjSRY8eO8sQT3XjssV40a3YTxYoVJzLyP778cio//vg95ctX\ncHdz8u6O1qHDPSxYMI/IyB0sWbKQmJgYHn20B3XrXk5SUhJr167ho4/eZc+eXe515M/v251t9+5d\n9Ov3JFFRB9xdx1q1akO5cuU5fPgQixf/xGeffcrKlSvYtGkj48e/T506dX3WMXfubN58cxwpKSnU\nqFGTnj2f5OqrG1GgQH62bdvGtGmf8fPPS5k6dTLGbOHVV9/IsaBTeHh4uvYF4j36mTfvIFTaLpsu\nrlHLwHaDc/GuKTZ69Cv07z+Ihg2v8TmmypevwLPPvpBunevWrXWP9OYa0SyQ66+/wV17a+3a37M1\n4JQvn+fEmplMnOzM2nn//beZOXM6YI/54cNHU7p06ZCXj44+TP/+vdm1aycAtWsL/foNDGnZbt0e\np1KlSiQlJbF+/Z/MmjWdyMgdjBkzgl27InnyyadDWk9SUhIjRrzkDkwWK1aMESPG5HjAVSmllFIq\nt0tbDPxIbPAbn6WKFeT4iURKF4+gTrXSdGpVmyIRNmyS1wuAZ4UGnC5gaT88oVTJzw2efXaoT6Dg\n1KlTbNnyN9OmfU50dDQFCxakVas23HvvA0EvbPfs2Z3lGk6urkiuejbJyckkJycHHaXLe7SpiIjg\n2SBpPfJIDw4ejOKbb+Zx5Eg0Y8eOZOxY33lq167Dww9354UXBgG+XZgiIgoxduybPPNMH/bu3cPa\ntb/7dAUCGwR49NHHiIo6wHffLaBwYd8uiMOGDSEq6gDh4eG8/vrbPjVyKlasRJcuj9CoUWP69OlJ\nbOxxhg4dzLRpX7kzX7Zu/ccdbKpVqzbvv/+JTzfEK66ox6hRr/HBB28zbdpUfv99FZMmTaRXr6cy\nta9CVapUqZAv6CtW9F+42TuoF6i2lvc83kXHa9WqzXXXNWXVqt+IjNzB00/3omTJklxzTWMaNWpM\n48bXBSwY/e+/xv3Y9fcOxb59nlE1jh+PISrqQJC5/StcuIg7I6tIEc8xltFoagkJnukZZUOFIikp\niTfeGMOCBXMBG0B86aURXHVVw5DXsXfvHvdnAqBateq8/vo7GRbnd2nd2lOnqUGDq2nTph29e/cg\nKuoA06ZNpUmTphkWRz916hTDhg3ht99+BWzX27Fj38qRQQ6UUkoppfKShNPJGRYD91a2RCFeeqQR\npxKS/PYeyusFwLNCA04XqGAfnmBV8nODypWrUKuW+LxWv34Dbr31Nvr2fZxdu3byzjtvsHPnfwwa\n9HzA9YwZM8Jd5Doznn9+GG3btgd86zbFx5+iaNHAdWO8s1uKFy+RqW3my5eP5557kUaNGjNt2uds\n3eoJOFSsWIn//e9uHnjgQVauXOF+vUyZMj7rqFy5Cp98MpXPP5/E999/4+5+FxYWxtVXN6Jr10dp\n1KgxQ4YMAKB06bLuZTdt2uDeZtu27QMWZK5T53I6derK5Mkfc+DAflasWO4erW7u3K9ISUkBoH//\nwQFrXvXs2dtd82jevDk89livLNXDyUhGNYdcQhkd0DVfZg0fPpo33xzLTz/9QGpqKjExMSxZspAl\nSxYCtltVy5ZtuOee+3z2l6uwe2bFxnpqLv366/J0NbhC0aDB1bz3ns2Y8t6Hp07FB13OuybYmdaO\nOnnyBC++OITVq213xgIFCjBs2EifGlcZ2bRpA889N8D9ObjkkpqMH/8BZcqUzWDJwC6++GIGDHiO\nwYP7AfDtt/ODBpyiow8zeHB/dze6okWLMm7c21x1VYMst0EppZRSSlkxcQkc8VNzKZCGtctRvEhB\nihfJoFREHi0AnhUacLpABfvwhFIlPzcqV64cY8eOp3v3rpw8eYJ58+Zw8cWV6Nr1kRzbpncGSlRU\nFDVrBg44uUaQCwsLo1y5wCPNBdOy5W20bHkbMTHHOHr0KCVLlvTpsrVzZ6T7ccWK6es8FS9enN69\nn6ZXr6c4ePAgiYnxVKhwsU9Bddc6KlWq5H5ty5bN7sdNmzYL2sabbmrO5MkfA7B589/ugNM//2x2\n2lCC+vUDX1Dnz5+fG264iVmzphMXF8uuXTsD1oM6E6F268pKIClURYsW48UXR9C9+xMsXbqI3377\nlb//3uiub7V9+za2b3+Pr7+exbvvTqBy5SoAJCd76l+9+urrATOh/G0vO3mPTOc9QqI/UVGe6Vk9\n/sGOjDdoUD+2bdsK2IygkSPH0aRJxqPKuSxZsoiRI4e5s7Iuv7wer732VsBi+pnRpMn1FCpUiPj4\neLZv/zfgfDt2bGfQoKfdWWalS5fh9dffQaTOGbdBKaWUUkpByWIRlCkR4bfQd6GC4RQtlJ+jsQna\nNS4HacDpAhXsw5OXq+RXrVqNZ54ZzMiRwwD49NOPuPbaxtSpc3m6eV1ZGmfikktquh/v27cnaGDE\n1W3n4osrhdxlJ5CSJUv5vTjevHkjYGv/lCoV+OI5X758XHxx+mHsjx+PYc+e3QA+w7F7dz0sVqx4\n0LZ5B8Bco4YBnDx5ylk+46CHdw2eEyfigsyZO1SqVJkHH3yYBx98mJMnT/LXX+tYvXolS5Ys5MiR\naA4ejGLcuFG8/faHgG+GUKlSpdNl/IWibdv27ky9M2m3K7jiOr4D2bfPM71GjZpB5gxs166d9O/f\n247RKooAACAASURBVCdIM27ceOrWvSLkdcyZM4vx48e5uzc2bdqMV14Zk24US2+pqalERUWxb98e\nihcv4S787k94eDhFixYjPj4+4OAAmzZtYNCgfu6MsypVqvLGG++6A4pKKaWUUio0wQbQiigQTsPa\n5f0W+m5Wv6J2jTsL8m5l6Quc68PjT16vkt+mzR3uUbuSkpIYPXp4wBHRzpT3SFZ//bU+4HwnTsS5\nMzIy211mz57dTJz4AWPHjvSp3ZPWqVOn+OOP1UD6QtLLli3mvffe4s03x/pb1O2XX352d3vzXkep\nUp4AUEaBBVfRcvANHJUubQNghw8fIiEhePerQ4c83UW9A1i5SVJSErt27WTDBt/jpkiRIlx//Q30\n6zeQL76Y5R6RcO3aP9z7zTuw+fffG4NuZ9eunXz22af89NP37N69K1vfQ1hYmDvYs2HDep8aVWmt\nX78OsPWb6tZNHwDOyN69e+jb9wl3sKlKlap89NGkTAWbvv56Nm++Odbdzvbt7+LVV98IGmwCiImJ\noWPHdvTt+wQff/xB0HlPnjzh7qZXvnz6AQs2b97EM8885Q42XX55PT76aLIGm5RSSimlgNiTiWyJ\nPELsyeDFvZNTUpi2aCtDP17FkAmrGPrxKqYt2kqycy3jcn+Ly2jZqAplSxQiX5it09SyURXub3GZ\nu2tcXr52zmkacLqABfvw5HWDBj1P0aK2vsyOHdv58ssvcmQ7FStWcmdPLVr0I4mJ/r8Yv//+G5KT\n7fCbN910S6a2kZiYyOefT2LBgrksXrww4HyzZ89wj1x2221tfab9/fcmvvzyC+bMmcWuXZF+l09K\nSnLvp4oVK/l0e/MuxPzjj98Fbe/ChT/4Xa5+ffv49OnTLFmyKODyCQkJ/PLLMsAW4s6tF+IDBvSl\nc+d76Nevt099L28lSpSgXr367ucJCfb4uuaaa91d/b75Zl7QgOpnn33Kxx9/yCuvvMimTRuy8R1Y\nrrpJx44ddRe+TuvIkWhWrrTTmjS5PtMZfvHx8Qwe3I/Dh20gsnZt4cMPJ2Xq2Pjjj9WMHz/O/fyh\nh7rx7LMvhNRlslSpUlSvXgOA339fFbT7oPdn/dprfUcujI4+zLPPPsPJkycAaNz4Ot5++8Og2YhK\nKaWUUnlBYlISwyb9Tv93f+W1L9fT/91fGTbpdxID/M51DaAVfTyBVDwDaM1Yss1nPleh75GPNWF0\nz+sY+VgTOresnadHdT+bzvu9LCLhIjJJRFaIyK8iUk9ELnMe/yIiH4rIef8+coJ+eAIrV648PXr0\ncj+fMuUT9u/flyPbuuee+wCb2fPee+PTTd+5M5JJk2xNoypVqmZYAymtmjUvpVq16gDMnTubAwf2\np5vnzz/XMHmy7SLYoMHV6QoV33xzC/fjDz98L93yKSkpvPXWa/z33w4AHn64u8+FePXqNdzrXLdu\nLdOmfe63rStW/MJXX80EoEaNS2jUyHPB/b//3eUu/v3++28RGflfuuWTkpIYO3akO7Bwzz33hVxr\n6UJzww32OEhMTGDChPR/E7CBGtdogpUrV6FECVtsvmzZcrRqZUdIi4z8z6eLmLclSxa5A4Bly5al\nRYuW2f4+WrW6zd3F7623XufIkWif6UlJSYwbN8odDL3vvs6Z3sb777/tri1WuXIV3nrrQ5/suYzE\nxcUxatTL7uy9++/vTM+eT2aqDXfd1RFwHaOj/HaX27BhPR99ZP+WxYuX4H//u9tn+pgxIzh69AgA\n9erVZ8yYNylcuHC69SillFJK5TWjPv+T3QfjSHF+0qakwu6DcYz6PP0gTxkNoJVwOjnd65rNdG5c\nCDWc2gMYY24QkebAKCAMGGqMWSYiHwF3Al+fuyaeW1ol37+7776X779fwNathvj4eN58cyyvvfZ2\ntm+nTZs7+Oabefz11zrmzJnFvn176dChIyVLlmTjxg18/vkk4uJiyZcvHwMGPOd3xLVRo17m+++/\nAXxHwXN5/PHevPDCYOLi4nj88Ufo0uVRateuQ3z8KX79dTnz588hOTmZEiVK8txzL6Zbf716V3LD\nDTeyYsUv/PLLMvr1e5IOHe6hXLkK7Nu3hzlzZrmzX2688WbuuON/6dYxcOAQevZ8hNjY43zwwTus\nXbuG22+/g0qVKhMTc4yff17qzu4oWDCCIUOG+QStKleuwuOP9+H999/i2LFjPPbYw3TocA/XXtuE\nYsWKERn5H3PmzHIXF7/iiit54IEu6drRsWN7d9Bt1qz5VKxYKd08F4J27Towc+Z0DhzYz+zZM/jv\nvx20bdueihUrkZiYyI4d25g5czrR0TaA8+ijj/ks36dPf/78cw0HD0Yxb94c/v13K3fd1ZFq1Wpw\n9OgRVqxYznffLSAlJYWwsDAGDhxyxrXD/ClRoiRPPvkUY8aMZP/+vfTo8RAPPfQol10mHDwYxYwZ\n/+fu9nfbbW1p2PCadOv488819O37BOA7Ch7A/v37mD9/jvt5166PEhW1n6io9IFXb2XKlKVsWVuc\nfPbsL91BzIoVK9GqVZug3VNdatSoSYECBQDo0KEjixcvZOPGv1i9+jceeuh+OnXqSo0alxAfH8+K\nFcuZP/9rTp8+TXh4OEOHDncHCMEGo1yjSObPn58uXR5m5870Qde0Lrro4jMe1U8ppZRS6nwWezKR\nvYf8123deyiO2JOJPiPH6QBaF47zPuBkjJkrIt84T6sDx4CWwM/Oa98DrcnDASflX3h4OAMHDuGJ\nJ7qRkpLCypUrWLp0kXvUtOwSFhbG6NGvMWBAX/75ZzOrVv3GqlW/+cyTP39+Bg4ckq62UqhuvrkF\njz/em4kTPyA6Opq333493TwVK1Zi9OjXqVKlqt91DB36CgMH9uXvvzeyZs3vrFnze7p5br21Nc8/\nP8xvVlHVqtV4++0PeOGFwezfv4/Vq39zD0vvrVSp0gwfPporrqiXblqnTl1ITU1lwoT3OHXqJNOn\nT2X69Knp5rv22iYMHz6aggWDD0l6IStSpAhjx45n4MC+HDp0kLVr/2Dt2j/SzRceHk6PHk/Qps0d\nPq+XKlWK99//mCFDBrJt21Y2b97E5s2b0i0fERHBwIFDuPHG5jn1VmjXrgNRUVFMmfIJBw9G8frr\nY9LN07RpMwYPfj7T6/722/nuLmpgs4RC8eijj9G9++MAzJ/vOT3s37+PHj0eCmkd3gHN/PnzM27c\nWwwbNoTff1/F7t27GDduVLplihcvwdChw9115FzmzfMEzZKSknjuuQEhtcFfAFoppZRS6kIUqMD3\nHq/MprRSUu30ujU8dV11AK0Lx3kfcAIwxiSJyGfAXUBHoJUx5v/Zu/f4Nuv77v9vSZYu+SA7Pijk\nSAtxdKUcAgqB0gZqCKbp2Chs6Y+0bqGj67bfuvte9+h279TT2nu993tsu7vd29re96MbbSl3ID2s\nrO26Ukw4lY5SJ+Y04FICLeAciG05thRHl2RJvz9kKZYt+SRbkuXX8/FIJV3Hr4wazFvfz+eb/UhG\nJM369W9ra4Pq6pg6t1T8/tlXKVsOXq8793zNmoZ5j6Gr62267bbbdN9990mS/uEfPq+bbrpxXiul\nLYTf79O3v/1NfeMb39D3v/99HT16VOPj4/L7/br66qt15513KhAovrLV1Pfn83kLvr+Pfez3dP31\n1+ruu+9WX1+fwuGwvF6vtm7dqne9613at2/frOU5fr9PBw7cq29+85v63ve+p1AopFgspra2NgWD\nQe3bt0+7du2a431eqR/+8N/1rW99Sw8++KBCoZDGxsbU2NioCy+8ULt379b73vc++XzF//l89KMf\n0a23/rLuuecePfnkkxoYGFAikVB7e7suu+wy3XLLLbrhhhuKnu9ynSsZbWtrXPDnMXu+y+Wc9VyP\n59xfj8WOq68/F4gVG8uaNee+XZn+z9bvD+qHP/x33XfffXrkkUd09OhRRSIR1dfXa926dXr729+u\nffv2acuWwqsf+v2mvvvd+/X9739fP/zhD/X8889rZGREdXV12rx5s3bt2qX3v//92ry5cAi5lP7k\nT/5Qe/bcoHvuuUd9fX0aHh5WfX293vKWt2jv3r1697vfXbQ8curPyOOpy/sZ/eIXRwudMqfGRkN+\nv0/hcHjWnkuzmf7P1O/36e67v6oHH3xQ3/nOd/Tss89qdHRUDQ0NuuCCC3Tdddepp6dHLS0z/5X0\nyitHFjWGYn8fZMcDrHR8jlEL+ByjFizn5ziZTOmu7/2nnnz+hAZPn5V/Tb2uvmS9PnTzxXK5nPLU\ne+R0StP6fUuSnE7psresmxEi7bpso777+Cszjt912QZt2kB/zGrhmG1VoWpjmuY6ST+V1GxZVuvk\ntluUCaD+S7HzBgcjK+dNVjm/36fBwUilh4FV7t5779EXvvB3+rd/61VLy8L/hcLnGCsdn2HUAj7H\nqAV8jlELlvtzvL83pN6+mStdd+/cpJ7uzJfyn77rKb1+amZZ3ea1TfrMh66asT2ZSunAwaPqDw1p\nJBJTq8+rYKBD+3Z30tO4zPx+X9Gmu1U/w8k0zdslbbIs6y8ljUtKSeozTfM6y7IekfRLkh6u4BAB\nlNnPf/6yGhsbFxU2AQAAACiPuRp87+3aIsPt0sfv2KHP3X1YxwYz5XVOh7TR36SP37Gj4LnZBbT2\ndm0pWKaH6lD1gZOkf5H0FdM0H5PklvT7kl6U9GXTND2Tz79VwfEBKKNnnulXb++P6GsDAAAAVLn5\nNvj21NXpMx+6SpHxuAZORbVpbVNeo/BiWECrulV94GRZ1hlJtxXY1VXusQCovH/8x7/VRRddrI98\n5PcqPRQAAABgVSjW8HsuC23w7Wvw5DUIx8pW9YETAEz1N3/z92pubinagBoAAADA0jjXK2lQ4TFb\nbc2GggH/vHslGW6XggF/wR5OwUAHZXA1jsAJwIpC3yYAAACgPO576IgeOnQs93p4zFZv34DS6bTe\nf6M5r2vs290pSQUbfKO2ETgBAAAAAIA8diKpJ547WXDfj589oXfvumBefZZo8L16sV4gAAAAAADI\nM3j6rGLxZMF9diKlT/3zT7W/N6RkKjWv62UbfBM2rR4ETgAAAAAAIF86Pevu0TMJ9fYN6MDBo2Ua\nEFYaAicAAAAAAFYxO5HUqZFx2YlzM5r8rQ3yeuaODPpDQ3nnAVn0cAIAAAAAYBVKplL68v3P6Yln\njs1Yhc5wu/T2S9fr4JSm4YWMRGIajdpa29pQplFjpSBwAgAAAABgFbETSY1GbT3ws9f18OGZq9Al\nkyntuep8/do7tsjpcOiwdUrhSLzgtVp9XrU0GeUaOlYQAicAAAAAAFaBZCqlAwePqj80qOExW05H\n4eMeffq4Huk/npvx9NkPv1X7Hzyinzw/c9W6YKCDRuAoiMAJAAAAAIBV4MDBo+rtG8i9ThXpC57d\nnp3xJEl33rRNDd469YeGNBKJqdXnVTDQoX27O5d72FihCJwAAAAAAKhxdiKp/tDgos7tDw1pb9cW\n9XQHtLdri0ajtlqaDGY2YVasUgcAAAAAQI0bjdoKj9mLOjfbGFySDLdLa1sbCJswJwInAAAAAABq\nXEuTobbmws29s72civV0ojE4FoPACQAAAACAVcA8v7Xg9q7LN+j/++2r1RXcWHA/jcGxGPRwAgAA\nAABgBbETyXn3UZq6Ml14zJbXkznejifV1uzVrss26Oa3nS+X06me7q1yOR00BseSIHACAAAAAGAF\nmB4etTUbCgb82re7Uy5n4QKm6SvTxeJJSdKuS9bpA3tMbdqwRoODEUmaDJ1oDI6lQeAEAAAAAEAV\nmGvm0vTwaHjMzr3u6Q4UvF6xleleeu100XFkG4MDpSBwAgAAAACggsbthPY/eEQvvRrWSCRecObS\nbOFRf2hIe7u2zAipZluZLrvy3KalfStADoETAAAAAAAVMG5P6N4HQ+qzTslOpHLbC81cCo/FNDxH\neDR9VlJ2ZbpC57HyHJYbgRMAAAAAAGWU7cX042ePKxZPFT3usDWod1y2Qf419eo9NFD0uGLhkeF2\nKRjw55XhZbHyHJYbgRMAAAAAAGU0vRdTMeGIrU//81Nq9Xk0bieLHrd9S1vR8Ci7whwrz6HcCJwA\nAAAAAFhCdiKpwdNnFU9MyOOuk39NfS4Qmq0XUyFpSeFIfNZjunduLrqPledQKQROAAAAAAAsgWQq\npfseOqIfP3dC9pRSOcPt1DXb1+u9N2ydtZH3YrQ3e9XW7J3zOFaeQ7kROAEAAAAAsAQOHDyqhw4d\nm7HdTqT00KFjcjgc2tu1pWgj78WgFxOqlbPSAwAAAAAAYKWbT6lcdn8w4F/UPbwel9qbDTkdmZlN\n3Ts30YsJVYsZTgAAAAAAzJOdSBbshTSfUrlwxNZo1J7RyNtd55SdKL5aXdY129fTiwkrBoETAAAA\nAABzSKZSOnDwqPpDgwqP2WprNrTt/Fa978aAGow6tTQZc5bKtfkMtTQZMxp5NzW4df/jP88FUGua\nDDXWuzUeS2gkYuetLOdyOunFhBWBwAkAAAAAgDkcOHhUvX0DudfDY7aeeP6kDoVO6ZrtG7Rvd6eC\nAX/eMdMFA/68WUlTG3kXWkmu2GwqYCUgcAIAAAAAYFKhkGe2/kyxeCoXMu3b3al0Oq0nnjuhWIFV\n6ubqtzR9JTlWlsNKRuAEAAAAAFj1CpXMBQN+7dvdOa/+TP2hIe3t2qL332jqPdd1avD0WcUTE5LD\nIU+dS/419XI5WbcLqweBEwAAAABg1StUMpd9vbdry5z9mUYiMY1Gba1tbZDhdml9e0PRAIvgCasB\nn3IAAAAAwKo2W8lcf2hIUqb/0mxafV61NBm519kAa3jMVlrnAqwDB48u2biBakbgBAAAAACoaXYi\nqVMj44qMxzVwKqKBwajsRDK3PzwWKzp7aSQS0+Dps7o+uFHX79gor6dw8+5goGNePZ/6Q0N59wZq\nFSV1AAAAAICalO3LdNg6pXAknrfP63Fp16Xr9N4btqq37/Wi1/C4Xfq7bzytkUhcbc2Grr54rWKJ\nlEKvntbpqK1Wn1fBQEdeQ/DZej5NLb0DahmBEwAAAACgJk3vyzRVLJ7UQ4eOKZWWnn15uOg1YvGk\nYvHMjKThMVuP9J9Q985N+txvXT1jNbusliajaM+n6aV3QK2ipA4AAAAAUHNmK2ubqj80OGsz8MLn\nZPo6ZRuET2e4XUV7Pk0tvQNqGTOcAAAAAAA1xU4k9cqx0aJlbVOdjsa1psmj09H4nMdmzacsLlti\n1x8a0kgkVrD0DqhlBE4AAAAAgJqQ7dmUnbXkdEjp9OznOB3S9s52Pfb0iRn7vB6nYvHUjO3zKYtz\nOZ3q6Q5ob9eWoqV3QC0jcAIAAAAA1ITpPZtSc4RN2WP2XHm+PHWuGbORUum0Dh46NuOchZTFGW4X\nDcKxKhE4AQAAAABWvPn2bJquzWeordlbcDZSMpWS0+GgLA5YBAInAAAAAEDVsxPJWUvTRqP2vHo2\nTbfD9OeuN302EmVxwOIROAEAAAAAqlYyldI9PwqpPzSoyHhCbc2GggG/9u3u1EQynQuCWpoMtfo8\nCkfm1/zb6ZC6Lt8wr9lKlMUBC0fgBAAAAACoCtNnMcUnJvSHX/iJomcncscMj9nq7RuQ9dppjccS\nCo/ZuRCqod4978ApLWnPVefL5XQu07sBVjcCJwAAAABARU1dXW5qgPTCL8J5YdNUr5+K5p5nQyjD\nM//wqG0eK80BWDwCJwAAAABARWRnND3w1Gt6uP94bns2QFrw9eKpeR+7kJXmACwcgRMAAAAAoKyS\nqZT29x7R06EhnY7acjjKd2/D49S12+fXuwnA4hE4AQAAAADKJplK6bNf7csriUunl+baXo9LsXiy\n6P71bQ36+AevUIPhXpobAiiKwAkAAAAAUDb7HwzlhU2LUedyaCI5M6V6+6Xr5HQ41B8a1PCYLadD\nSqWllka3dgT86rkxQJNwoEwInAAAAAAAZTFuT+iJ50+UdI2N/kb92e079J3Hfq7+0JBGIjG1+rwK\nBjq0b3enXE6n9nZt0WjUVr1Rp7P2RG7VOwDlQ+AEAAAAAFh2diKpu77/guKJxdfPfeRXL9FOc60k\nqac7kAuWpgdKhtulta0NkiRfg6e0gQNYFAInAAAAAMCySaZSOnDwaK7MbTYel5RISoUiKadDMjev\nyds2NVgCUF0oXgUAAAAAlMROJHVqZFx2Ijnj9YGDR9XbNzBn2CRJ8aS0rq1wgLTR38RsJWAFYYYT\nAAAAAGBe7EQyr4Rt6uyl8JitVp9HjfUejccSuddn7Il5X9/pkD723sv1v775jAYGz+S2u5wObd3U\nrGQqRdNvYIUgcAIAAAAAzKpQsLTtTW3yuB16pP9cE/BwJK5wJJ73eiFSaSmZTGnbm1rzAqdkKq2D\nh4/L6XSqpztQ+hsCsOyIhgEAAAAAs5paFpdWJkj6yfMn88KmpdDebKjeqFN/aLDg/v7QUK5sD0B1\nI3ACAAAAABRlJ5JFA6ClFgz4ddaeULhIv6eRSEyj0bl7QQGoPErqAAAAAABFjUbteTX8XijD7VSj\n163TUVutPq+CgQ7t292piWRabc1GwXu2+rxqaTKWfCwAlh6BEwAAAAAgJ9sYvN6o01l7QvVGnbwe\np2Lx1JLe59rLNmhv15a8JuSS5HJmZjr19g3MOCcY6MgdB6C6ETgBAAAAwCqWDZiaGjy6//FXdNg6\npXAkLoektKRWn0eJifmFTS2NbjU3GhqPJTQSycxcumxruxySnj4yrJFILG82k8vp1NrWhhnX2be7\nU1KmZ9P0cwCsDAROAAAAALAKTV95zpg2iyk9+Tgyz5XmWpsM/fmHrpSvwZMLsabOXHrPdTO3FeOa\nXI2u0AwoACsDgRMAAAAArBJTg6BvP/pyXtlaqSVzV2zzy9fgkSQZbteMmUuFts1lMecAqA4ETgAA\nAABQ46bPZmprNnQmlliSa7c3U+4GYCYCJwAAAACocQcOHs2bzbTYVedaGj2KjMfV6vNq+5Y2de/c\nrLZmL+VuAGYgcAIAAACAGmYnkjpsnSr5Ol6PS5/9jat01p6gpxKAORE4AQAAAMAKUqghd7Fjmho8\nuvfBkMLzbPw9m12XrpOvwZPr0wQAsyFwAgAAAIAVoFAfpmDAr327O+VyOmUnkgqPxdR7aEDPHh0q\nuPLcYl190Xl67w1bl+BdAFgtCJwAAAAAYAUo1Iept29A6XRaDodD/aHBGb2ZliJskqQbd26Sy+lc\nkmsBWB0InAAAAACgytmJpPpDgwX3Pf7sCcUTSxMsFeOuI2wCsDD8rQEAAAAAVcJOJHVqZFx2Ipm3\nfTRqK1xkZbnlDpucTsnf2rCs9wBQe5jhBAAAAAAVNlt/polkWmfOJtTc6NbomUTZx+Z2MU8BwMIR\nOAEAAABAhRXrz/TiqyMaHo0pFk/OcvbySkykNBq1tZZZTgAWgMAJAAAAAMrMTiQ1GrXV0mRIUtH+\nTMcGzyz5vQ23U3YipTafR5cH/HJIevrI0IyG41mtPm9unAAwXwROAAAAAFAmhUrnzPNbi4Y9y6Gx\n3q2P375d/tYGGW6XJOk913Xq6w9Y+snzJ2ccHwx05I4DgPkicAIAAACAMilUOveT50/K5ZSSy9v7\nOyc8ZsvjduWFSIbbpTtv2qYGb536Q0MaicTU6vMqGOjQvt2d5RkYgJpC4AQAAAAAS2BqmVyhGUF2\nIlm0dK5cYZMkrWnyFCyRczmd6ukOaG/XllnfBwDMB4ETAAAAACxANljytdRLmn2FOZfz3Apv4bFY\nWUvniglunb1EznC7aBAOoGQETgAAAAAwD9ODJX9rvbZvaVc6ndZDh47ljsuuMCdJPd0BSZmQ6l8f\nf6Ui455q09pG9dwYqPQwAKwCBE4AAAAAMA/T+y+dGjmr3r4BeT2FZwsdtga169J1euTpY/qP504o\nPrH8Y3Q6pFRaavN51Fjv0XgsofCYrZYmj4IBv3q6t+bNugKA5ULgBAAAAABzmK3/UiyeLLg9HLH1\nma/0LeewZvidWy7R5vOacv2X5uorBQDLpaoDJ9M03ZLukvRmSYakv5D0uqTvSzoyediXLMs6UJEB\nAgAAAFgVRqO2wlXQf2k2TocUOH+NfA2e3Db6MQGolKoOnCR9QNKwZVm3m6bZJulpSZ+V9HnLsv5n\nZYcGAAAAYLVoaTLU1mxURdPvYjb6m/LCJgCopGov3v2mpE9OPndImpB0haRfNk3zMdM0/9k0TV/F\nRgcAAACgZtmJpE6NjMtOJGW4XQoG/BUbi9OReWxvNrR7xwZt8jfmtjkd0ua1Tfr4HTsqNj4AmK6q\nZzhZlhWVpMlQ6VuSPqFMad0/WZZ1yDTNj0v6tKQ/rNwoAQAAANSS6avRtTUbunxrh+xEGbp+T9Ha\nZOjPbt+hZCqteqNOZ+2JvF5MkfG4Bk5FtWktM5sAVB9HOp2u9BhmZZrmZknfkfRFy7LuMk1zjWVZ\npyf3XSTpHyzLumG2a0xMJNN1dTTIAwAAADC3L9//nL77+CuVHobefe2F+s1bL630MABgNo5iO6p6\nhpNpmudJ+pGk/2JZ1kOTmx8wTfO/Wpb1lKQbJB2a6zojI+PLOMrVxe/3aXAwUulhACXhc4yVjs8w\nagGfY1QrO5HUE88cq+gY2nyGdph+3fy28/n/CZYdfx+jFH5/8S5HVR04SfozSa2SPmmaZraX08ck\n/a1pmglJJyX9VqUGBwAAAKC2jEbtijYGv7yzXb99yyW5sjkAWKmqOnCyLOujkj5aYNeuco8FAAAA\nQO2rN+rkdEipCnQecTmlD998MWETgJpQ1YETAAAAACwHO5HUaNSe0Yw7ejZRkbBJkrqCG9Vg8J9o\nAGoDf5sBAAAAqGlTw6VwxNYDP31VoYFRhcfs3GymNp9Hl2xpV9+Lp8oyps1rm3TmbEIjEVutkz2b\n9u3uLMu9AaAcCJwAAAAA1Bw7kdTJ8Bk98NTrCr02onAkXvC47GymcCSux54+sej7uZxSMpVZdWfG\nwQAAIABJREFUrqnQBCnH5P+0+bwKBjq0b3enJpJpjUbt3OwqAKglBE4AAAAAakYyldK9Dx3RT547\noVg8tez3czqld1y2Qe+5rlPR8bgeeOo1Pdx/fMZx1wU3aM9V5+eFSy6ntLa1YdnHCACVQOAEAAAA\nYMXLls0VC3yWSyol1bmcajDq1GDUqefGgFwup/pDQxqJxNQ6ZUaTy+ks27gAoNIInAAAAACsWMlU\nSvt7j+jp0JBOR+2KjKE/NKS9XVtkuF1yOZ3q6Q5ob9cWyuUArGoETgAAAABWjOxMJpfToRPhcd33\n0BEdHxqv6JhGIjGNRu288jjD7aJcDsCqRuAEAAAAoOqN2wntf/CIXvj5kE6fmaj0cPK0+gy1NBmV\nHgYAVBUCJwAAAABVK5lK6cDBo/rxsycUiycrPZyCtp3fStkcAExD4AQAAACg6lSqCfhCeT0uve/G\nQKWHAQBVh8AJAAAAQNXIls699GpYI5G4HI7KjKOpvk6G26VwxNaaRkON9XUaGDwz47hrtq9Xg8F/\nVgHAdPzNCAAAAKDiipXOpdOVGc/VF6/LW2muzuXQgYNH1R8a0kgkplafV7su26Cb33Z+ZQYIAFWO\nwAkAAABARdmJpO55wNITz5+s9FBy+kND2tu1JW+luZ7uQF4ItWnDGg0ORio4SgCoXgROAAAAACoi\nO6vpsHVK4Ui80sPJMxKJaTRq5wVOkmS4XTO2AQBmInACAAAAUBEHDh5Vb99A2e/rkJSW1N5s6Ews\noVg8NeOYVp9XLU1G2ccGALWCwAkAAADAssiuNJcNbrLPDbdLdiKpw9apso9pR6BDH3zXNp21J9TS\nZOjbj75cMPQKBjpkuF1lHx8A1AoCJwAAAABLKlsq1x8a1PCYLa/HKckhO57UmiZDl21t19nYRNnL\n6Lwelz70yxepwaiTr8EjSdq3u1OS8pqBBwMdue0AgMUhcAIAAACwpKaXyk0tWRuJ2nqk/3glhqVr\ntq9Xg5H/n0Aup3NGM3BmNgFA6QicAAAAACwZO5FUf2iw0sNQo7dOXo9LIxF7XrOWaAYOAEuLwAkA\nAADAvE3ty1RoJtBo1FZ4zK7AyM5p9Nbp737vGk0k08xaAoAKIXACAAAAMKepfZnCY7bamg0FA37t\n292ZF+zUG3Va02RoJFqZ0GmTv1Gf+OAVcjmdcjnFrCUAqBACJwAAAABzmt6XaXjMVm/fgF56dURn\n7QkNj9nyuBxKpdOaSM1yoRI5JG30NyqwuUXPHA1rJBLTGp+hN6/z6QPvNLVmckU8AEBlETgBAAAA\nmFVkPK6+l04V3DcweCb3PJ5ML9sYWhpcuvOmi3TBhpbcCnP/z/Wzl/cBACqHwAkAAABAQdkyukMv\nDep0NF7RsVx8oV/bO/1522j0DQDVi8AJAAAAQEHTy+gqxetxqefGrZUeBgBgAQicAAAAAORkV6FL\nJlN66oU3Kj0cSdI129erwXBXehgAgAUgcAIAAACQK5/re/GkTp+ZqMgYnA4pNaUNVJvP0A4zsxIe\nAGBlIXACAAAAViE7kdTg6bNSOi1/a4O++chRHTx0rOzjuPqitbpt91bFE0nVG3U6a0/kHmkGDgAr\nF4ETAAAAUEOyJXHFwppkKqX7HjqiHz97QnYiJUky3E7FJ5+Xk0PSrddeqDVNRm5bdgW67CMAYGUi\ncAIAAABqQLYkrj80qPCYrbZmQ8FAphzN5XTmjrvvoSN6aNpMJrsCYZMktTV71TIlbAIA1A4CJwAA\nAKAGTF9RbnjMVm/fgJKptPZcuVn1Rp0GR8/q8WdOVHCU+YKBDkrmAKBGETgBAAAAK5ydSKo/NFhw\n38OHj+nhw+XvzTSb1iZDV2yjGTgA1DICJwAAAGCFG43aCo/ZlR7GvKxp8ujPP3QlPZoAoMY55z4E\nAAAAQDVraTLU1lz5XkgtDXVySGrzGdq8tqngMTu3rSVsAoBVgBlOAAAAQA3o3NSi4RdOVXQMf/C+\nHfLUOdXSZKjO5ZhsYj6kkUhMrT6vgoEOyugAYJUgcAIAAABWqGQqpf0PhtR/ZEino/GKjqW92ZB/\nTX1eE/Ce7oD2dm3RaNRWS5NBg3AAWEUInAAAAIAVxk4kdTJ8Rv/n/v/UyZGzZbvvzm1rVed06MkX\n3pixLxjwFwyUDLdLa1sbyjE8AEAVIXACAAAAVohkKqX7HjqiJ547qVg8Wbb7tjfnl8M1NbgplQMA\nzIrACQAAAFgh9j8Y0sP9x8t2v3Vt9frIr15KqRwAYMEInAAAAIAysxPJeYc1diKp8FhMP/rZ63rs\n6fKFTS2Nbn36zquKjo9SOQDAbAicAAAAgDJJplKTK7cNKjxmq63ZUDDg177dnXI5nZLOhVFNDR7d\n//gr6g8NanjMLvtYdxTpyQQAwHwQOAEAAABlcuDgUfX2DeReD4/Zudf7dnfqwMGjOmydUjgSl6dO\nik9UZpyb1zap58ZAZW4OAKgJBE4AAABAGdiJpPpDgwX39YeGlJhI6tGnT+S2VSJscjqld1y2Qe+/\nMZCbcQUAwGIQOAEAAABlMBq1FS5SGhcei+k/nn+jrOM5r61eiURKIxFbzY1uveVNrfrAHlMNhrus\n4wAA1CYCJwAAAKAMWpoMrWlyaySamLEvLSk+kSrbWAy3U39+51WSxEpzAIBlQeAEAAAALKGpK9BJ\nmUCn3uvW39zbXzBsqoRrtq/PBUysNAcAWA4ETgAAAMASmLoC3fCYLa/HKckhO56UwyGl0pUbm7vO\nqYmJVN6qeAAALCcCJwAAAGAJTF+BLhY/VyKXrlDY5HI6dF1wg371HRcqOp6gdA4AUDYETgAAAECJ\nZluBrpJaGj16z3WdMtwumoEDAMqKtU4BAACAebATSZ0aGZedSM7YN9sKdJV0OmprNFp94wIA1D5m\nOAEAAADKBEqDI+OSwyH/mvpc6dnU3kzhMTuvD5LLmfn+1uN2ye12KJ6oYKOmAlp93lzzcgAAyonA\nCQAAAKtaMpXSvQ8d0U+eO5Hru+T1uLTr0nV67w1bZ/RmGh6z1ds3oGQype6dm9V7aED/8fzJqgub\nJCkY6KBnEwCgIgicAAAAsKodOHhUBw8dy9sWiyf10KFjSqWlZ48OFTzv0aeP6+H+4+UYYlHOydXv\n2poNGXUu2YkJnY7G1erzKhjoYDU6AEDFEDgBAABg1YqMx9X34qmi+/tDgzodjRfcl6rAhKYrt/l1\n864L1NLo0Vl7QvVGnc7aE7nV5+xEUqNRm9XoAAAVR+AEAACAVSfbl6nvpVM6faZwoCSpaNhUbk6H\n1BXcqJ7urbm+Ub4GT96jJBlul9a2NlRkjAAATEXgBAAAgFVnel+mapdOS3uu3JwLmwAAqHb8GwsA\nAACrhp1IamAwqsNW8TK6SmltcsvjdhTc19bManMAgJWFGU4AAACoedkSuv7QoMJjtqptPbn/9t7L\ndeHGFn370ZcLzrxitTkAwEpD4AQAAICaV80ldGuaPLpwY4sMtyu3qlx/aEgjkRirzQEAViwCJwAA\nANQ0O5FUf2iw0sMoavuWttzsJZfTqZ7ugPZ2bWG1OQDAikbgBAAAgJo2GrUVHrMrPYyi9lz1phnb\nWG0OALDS0TQcAAAANa2lyZDhqc5ZQu3NhtqavZUeBgAAS27JZjiZpumQ5LUs6+y07e+X9CuSvJKe\nkvQly7JOL9V9AQAAAClTOleoDC2ZSik+kazgyIoLBvwFS+aKvRcAAFaKkgMn0zTrJf13SR+S9HFJ\nX5qy72uSPjDl8HdL+j3TNN9lWdYzpd4bAAAAmL4CXVuzoWDAr1uvvUAjkZj+x92HlEpVepT5nA6p\nK7hxRjPwYu9l3+5OuZwUJwAAVo6lmOH0r5JumHx+YXajaZo3SbpdUlqSQ1JKmRK+8yT9q2ma2yzL\nii3B/QEAALCKTV+BbnjMVm/fgB45PKCJKguasrou36Db32nO2F7svUhST3egbOMDAKBUJX1NYprm\nuyV1KxMovSLpZ1N2/7+TjxPKzGxqkHSnpLikzZI+XMq9AQAAsHrYiaROjYzLTiRnbC+2Al01hU0O\nR+ZPe7NX3Ts3qefGmeHRbO+lPzQ0470DAFDNSp3h9N7Jx/+U9HbLsiKSZJpmg6QblZnd9G+WZX1/\n8rivmaZ5taTflnSrpH8s8f4AAACoYeN2QvsfPKKXXg1rJBLPK5eLjicUPRvXcBWvQJd13eUbtOeq\n82ftyTTbanojkZhGozYr1wEAVoxSA6e3KRMqfT4bNk26TpIxue970875gTKB00Ul3hsAAAA1KtvL\n6MfPnlAsfm5mT7bE7PFnjstOpORwVHCQBVwX3KA6l1P9oSGNRGJq9XkVDHTMqwdTS5OhtmajYIDW\n6vOqpclYrmEDALDkSg2c/JOPL03b3j3l+UPT9r0x+dhe4r0BAABQo6b3MprOTmTq5dLpco2oMKdD\nSqWl9mnNvfd2bVnwKnOG26VgwF/wfQcDHaxWBwBYUUoNnLJf00yvkL9x8vFly7Jem7bvvMnHsyXe\nGwAAADVotl5G1aS5waNPfvAKJVPpGcGS4XYtqvwtu2pdoRlSAACsJKUGTq9L6pRkSvqpJJmmeb6k\ni5Upp/thgXOum3ycHkQBAABgFbATSZ0YOqNkIllw1s5o1F4RfZki43ElU+kl7avkcjrV0x1Y1Awp\nAACqSamB06OStkr6fdM0/8WyrKikT0zZ/y9TDzZN863KrF6XlvR4ifcGAADACjJuT+jeB0N66bUR\nhSO22nz5ZWiSFJ+Y0N9/65kKjzTD63EqnZLsIsvdtTUvX1+lxc6QAgCgWpQaOP0fSb8h6TJJr5im\neUrSW5QJlF6yLOsRSTJN8wJJn5Z0mySvpAlJ/7vEewMAAGAFONcA/Lhi8XPhTbYBeDKV1u3vNCVJ\nf/G1Qzo+XN7OC06nlCqQKV2zfYNuvfZCfe5rfToRHp+xn75KAAAUN/tSGXOwLOuQpD+dfNmhzMpz\nDklRSR+acmi7pDuUCZsk6U8ty3qulHsDAABgZcg2AJ8aNk31SP8x/fO//adefDWsgcEzZRuXwyGt\na6tXS4NbUqYBuJRpAN69c5P27e5Ug1Gnz374Kl0f3KA1TR45JLU3e3P7AQBAYaXOcJJlWX9lmuZ/\nSLpT0jplVqz7gmVZL085LLuK3TOSPmlZ1vdLvS8AAACq33wagKfT0hPPvaEnnntj1uOWiq++Thdd\n0K56w6VH+o/ntqcmV7zbvqVdPd2B3HaX06nb92zTbbuT9FUCAGCeSg6cJMmyrMc1S08my7Kipmme\nb1lW8bVtAQAAUBPsxLlgZjRqK1wlDcDrnNKnfv1K+Sd7I33iy08WPO7Zl8OyCzQ0p68SAADztySB\n03wQNgEAANSmbMDU1ODR/Y+/ov7QoMJjttqaDW3v7FCrz6NwJF7pYWrHtrXatNYnSTo1Ml40CBuJ\nxDQatQmXAAAoQdkCJwAAANSWbDPwbMDkcTtlJ/Kbgj98+JjW+DwVHGWGy+nQHXvM3OuWJkNtzYaG\nC4ROrb7lW30OAIDVYkkCJ9M0r5L0QWVWq/NNXtcxx2lpy7IuXor7AwAAoDymlst9+9GX1ds3MGVf\n4abgp6tgdtN1wQ1qMNy514bbpWDAnzf+LFafAwCgdCUHTqZpfkbSJ6Ztni1sSk/uT5d6bwAAAJTH\n9NlMbc2GzsQSlR7WnNqaDe0I+AuuKJfd1h8a0kgkplafV8FAB6vPAQCwBEoKnEzTvE7SJ5UfIo1I\nimoJAiXTNN2S7pL0ZkmGpL+Q9IKkr05e/3lJv2tZVuGv0wAAALAkDhw8mjcbqFApWrW5+pLz9ME9\n24rOVnI5nerpDmhv1xZWnwMAYImVOsPpI5OPaUl/IunLlmWdLvGaU31A0rBlWbebptkm6enJP5+w\nLOsR0zT/t6RbJH1nCe8JAACwak0tmcuGL3Yiqf7QYIVHNn+G26lrtq/Xe2/YKpfTOY/jWX0OAICl\nVmrgdI0yYdOXLMv66yUYz3TflPStyecOSROSrpD06OS2f5f0ThE4AQAAlKRQyVxwshRtNGoXXdGt\nWhgepy7v7NC73vomrWtrYKYSAAAVVmrg1Db5+C+lDqQQy7KikmSapk+Z4OkTkv7GsqxsuV5EUstc\n12ltbVBdHb90LBW/31fpIQAl43OMlY7PMJbal+9/bkbJXG/fgDyeOt1588XqaK3X4MjZCo5wprWt\n9frjO66Ux+3UuvZGeT0swIzy4+9j1AI+x1gOpf5beUjSeknjSzCWgkzT3KzMDKYvWpa13zTNv5qy\n2ydpzhK+kZFlG96q4/f7NDgYqfQwgJLwOcZKx2cYS81OJPXEM8cK7vvBT36hQy+e1PDpWJlHNbft\nW9rVWp/5dTYyelb8vwLlxt/HqAV8jlGK2cLKuYvaZ/fk5ONVJV6nINM0z5P0I0l/bFnWXZOb+yeb\nlUvSL0l6fDnuDQAAsFrMVTL3xkhMqQquL7xpbaO6Ll+vNU0eOSS1N3vVvXMTq8kBAFDFSp3h9EVJ\nvybpY6Zpfs2yrLElGNNUfyapVdInTdP85OS2j0r6e9M0PZJe1LkeTwAAAFiEeqNOzY1ujZ5JVHoo\nM7zj8vW6/Z2mXE5nwYbmAACgOpUUOFmWdXCyxO2PJD1umuYfSXrYsqz4UgzOsqyPKhMwTde1FNcH\nAABYzbKNwg9bp6oybHJIuumtb8qtNMdqcgAArBwlBU6maX5+8ulJSZdK+oGkCdM035AUneP0tGVZ\nF5dyfwAAACzegYNH8xqFV0qdy6GJ5MyavbZmr1qajAqMCAAAlKrUkrrfl5T97SCtzBdRbkmbZjkn\ne1wFOwEAAACsLtPL0SLjcfW9dKqiY2pvNhQM+JVKp3Xw0Mym5cFAB6VzAACsUKUGTq+J4AgAAKBq\nZcvm+kODCo/ZavV51FjvUWQ8rtPRJemCsGDntXv1qTuvljOdluF2KZlKyelwqD80pJFITK0+r4KB\nDpqCAwCwgpXaw+nNSzQOAAAALIPpZXPhSFzhSPmDpkbDqQvWN+vd116ozWt92rS+JbcMt8vpVE93\nQHu7ttAUHACAGlHqDCcAAABUWLHV2+xEUv2hwQqO7JzW5nqdCJ/VX379sNqaDe26bKNuftv5uYbg\nEk3BAQCoJQROAAAAK9T0crm2yZ5I+3Z3yuV06mR4XMNjdqWHKUkaGDyTez48Zuu7j7+i8bNx9XQH\nKjgqAACwXJYscDJN0yvpg5J+SZkV69okpSSFJb0k6UFJX7Msa3Sp7gkAALCaTS+XGx6z1ds3oMh4\nXEpLP7Mq2xR8Lv2hIe3t2kL5HAAANWhJAifTNHdLukfSeZObHFN2t0q6UNJNkv7MNM3bLct6cCnu\nCwAAsFrNVi730xeqO2jKGonENBq1KaMDAKAGOec+ZHamae6R9ENlwibH5J9XJP2HpKckvTpl+1pJ\n/26aZnep9wUAAFitkqmUvv6AVTXlcovV6vOqpcmo9DAAAMAyKGmGk2maayTtn7xOXNL/kPQly7IG\npx23TtLvSPpjSR5J95imaVJeBwAAUFi2EXi9UafRqC05HPKvqZfhdunAwaP6yfMnKz3Eojb6GxWz\nkxqJxNTq86rBW6fXT0VnHBcMdFBOBwBAjSq1pO53lSmZm5D0K5Zl9RY6yLKsk5I+bZrm45J+IMkv\n6QOSvlDi/QEAAGpKthH4YeuUwpF43j6vx6WrLz5Pzx4dqtDo8jkc0s5tfr08MKbTUVutPq+CgQ7t\n292piWQ6t3Jencsx2dx8KBdC7bpsg25+2/mVfgsAAGCZlBo4/bKktKS7ioVNU1mW1Wua5l2SfkvS\nbSJwAgAAyDO9EfhUsXhSj/QfL/OIinNI2vuOLWppMnLhUnbGksupvN5MPd0B7e3akjtu04Y1GhyM\nVGjkAABguZXawym7ju13FnBO9tjOEu8NAABQVnYiqVMj47ITyWW5fmQ8rr6XVkbDb+lcDybD7cqF\nS7P9fLLHUUYHAEDtK3WGU9PkY3gB52SPbSvx3gAAAGWRLXPrDw0qPGarrdlQMODXvt2dcjlL+/7O\nTiQVHoupt+91PX1kWKej8blPKrNNaxs1cOrMjO3ZHkzL+fMBAAArU6mB07CkdZK2SvrZPM/ZOuVc\nAACAqje9zG14zM697ukOFDttVlNDmmpebW59W4P+2/t26HtP/DyvB1O2V5O0PD8fAACwspUaOP1M\n0ruV6cm0f57n/LYyfZ8OlXhvAACAZWcnkuoPDRbc1x8a0t6uLXOWiGVXnJva42i2Xk3V5ER4XJ/9\nylMKBvz6zG9cqeh4Iu99LMXPBwAA1J5SA6f9ygRO15qm+XlJf2BZVrrYwaZp/rWka5UJnA6UeG8A\nAIBlNxq1FS4yA2kkEtNo1M5rjj3V9FKzNU2GLg90aG/XhUVDmmo024ylUn4+AACgdpUaOH1L0lOS\nrpL0UUnXm6b5T5KelJTteLlW0lslfVjSZcqETf2S7i3x3gAAAMuupclQW7NRsOwt2zS7mOmzmEai\nth4+fEwv/iJc1WV0xRSasVTKzwcAANSukro4WpaVknSbpKPKrIy7XdLfKxNC/WLyz1OS/kGZsMkh\n6VVJt842EwoAAKBaGG6XggF/wX3ZptmFzFZqdjJ8dsnGt1h1zswvZguRnbE01WJ/PgAAoLaVOsNJ\nlmW9Zprm2yX9paQPznLNhKT/q0zZ3Uip9wUAACiXbHPsYk2zs6b2apqt1KySPvfhq+RyOdXSZCiZ\nSmn/g0f00qsjOh215XG7lE6nZSdSBc8tNmNpvj8fAACwepQcOEmSZVlDkn7TNM0/lbRb0iWS2pX5\n4iws6VlJD1uWtXKaFQAAAExyOZ3q6Q5ob9eWGc2/7URS4bGYeg8N6NmjQwqP2WprNtS5aY3cdU7F\nJwqHN5WwaW2j1nc0Tdni0od/5aK8oEyS7nnA0hPPn5xxfrEZS7P9fAAAwOq0JIFT1mTw9I3JPwAA\nADXFcLtyDbCnNgSf3r9oeMzW8AtvVGKIRW1a26hP3HFFwX1T35ck/fpN21TvrVvwjKXp1wEAAKvX\nkgZOAAAAq8X0huDV6rLOdn3oprfI1+CZ9znMWAIAAKWaV+BkmuZt2eeWZX2j0PbFmHotAACAajO1\n1Gxq4DJbQ/Bq4XRIXZdvUM+NAbmci1snhhlLAABgseY7w+k+SenJP98osH0xpl8LAACgKkwtl8v2\nZAoG/Nq3u1Mup1OjUXtGGV216Qpu1O3vNCs9DAAAsEotpKSu2Mq5C11RFwAAoKpNL5cbHrPV2zeg\ns7EJve/Grfr3n75awdEV5nBI6bTUPiUcAwAAqJT5Bk53LnA7AADAijRbudwTz5/UUy+dVGKizIOa\nxSZ/o37n1kvUVO/WWXuCfksAAKAqzCtwsizrawvZDgAAsFKNRm2FZymXq1TYVOdyaCI5s5PBWTup\ntmavDLdrQY3BAQAAltPiOkiWyDTNN5mmeU0l7g0AAFBMMpXSD376qhxV1jDA43YoWSBskqSRSEyj\n0eruJwUAAFafhfRwmsE0zZSklKQdlmU9O89zrpH0qKTXJb25lPsDAAAslWQqpc9+tU+vn4pWeigz\n7LpkvZ59ebhgo/JWn1ctTUYFRgUAAFDcUsxwWuh3gMnJc85bgnsDAAAsia8/8FLVhU3tzV5179yk\nnhsDCgb8BY8JBjro2QQAAKrOvGY4maa5TlJglkN2mqa5Zh6XapL0B5PPq+s3OgAAsCrYiaRGo3au\nuXYyldL+3iN6/JmTlR5azvU7NmrPlZvzGoBnV53rDw1pJBJTq8+rYKCD1egAAEBVmm9J3YSk70gq\nFCo5JH15gfdNS/rxAs8BAABYkKnhUp3LoQMHj6o/NKjwmK22ZkPBgF+pVEoPHz5e6aFKkrwel3Zd\nuk7vvWGrXM78iegup1M93QHt7dqSF5gBAABUo/muUjdkmuYnJf1jkUMWWlY3IOmPFngOAADADNNn\nLEmZfkzTw6UGrzuvZG54zFZv30Clhp3HU+fUH/cEtcHfNGeIZLhdWtvaUKaRAQAALM5CmoZ/SdKY\npKm/BX1FmdlKfy7ptTnOT0myJZ2Q9DPLsmILuDcAAECeQqFSMODXvt2dOnDwaF6YNDxmF2y4XS0S\nEyk11ruZsQQAAGrGvAMny7LSku6Zus00za9MPv3X+a5SBwAAUIrsjKYHnnpND/efK4XLzliKjicU\nen2kgiNcuLZmVpoDAAC1ZSEznAq5fvLx5VIHAgAAMJvpM5ocRQr6n3zhjfIObAmw0hwAAKg1JQVO\nlmU9mn1umuYuSXssy/rU9ONM0/yipEZJX7Ysi2bhAABgwaaXyaXTFRzMIjgcmTEbbqccDofiiSQr\nzQEAgJpV6gwnmabZLOn/Srpp8vVfWZYVnXbYtZIukvQB0zS/Luk3LctKlHpvAACwOtiJpPpDg5Ue\nxqK1NHr0iTuuUDKVzpXOsdIcAACoZSUFTqZpOiT9m6S369xKdRdKmt7P6fTko0PS7ZIMSe8r5d4A\nAGD1CI/Fqrrpd1ZTfZ2iZydmbL/yLWvV3lKft42V5gAAQC1zlnj+HZJ2TT7vlXRZoebhlmVdK2mz\nMuGUQ9JtpmneVOK9AQDAKtF7aGDug6pA9OyENq9tUnuzV06H1N7sVffOTZTMAQCAVafUkroPTD4+\nJeldlmWlih1oWdZx0zTfPXnsDkm/JekHJd4fAADUODuR1DNHVk453XhsQp/69Z06a09QMgcAAFat\nUmc4XSYpLelvZwubsizLSkv6X8rMcnprifcGAABVzk4kdWpkXHYiuejzRqO2wpH4Mo1w6Y1EYjpr\nT+RK5hbz/gEAAFa6Umc4NU8+/nwB5xyZfGwr8d4AAKBKJVMpHTh4VP2hQYXHbLU1GwoG/Nq3u1Mu\nZ/Hvu6aeNzxma02TRxdf0CqnQ0pVyap0njqH3nbpOj3/crhgX6lWn1dNDR7t7w0t+P0DAADUilID\np5PK9GbaJOln8zynY/JxtMR7AwCAKnXg4FH19p3ruzQ8Zude93QHip63/8GQHu4/nnu44jyeAAAg\nAElEQVR9OhrXE8+9sXwDXYArtnbolmsvkL+1QYbbpf29obz3mBUMdOj+x19Z1PsHAACoFaV+xfbi\n5OPtCzjnvZOPz5d4bwAAUIXsRFL9ocI9l/pDQ3nlZdnSuXE7oa//yNKjTx8veF41+M9XR3JhkyTt\n292p7p2bZjQIv/XaC+b9/gEAAGpVqTOc7pG0R9Itpmn+vmVZfzfbwaZp3impR5m+T98u8d4AAKAK\njUZthQuUmklSeCymV46Nan1Ho779yMt66bURhcdsGR6XYvHqDmJi8aQGR8a1aa1PkuRyOtXTHdDe\nri0ajdq5BuGnRsaLvv+RSEyjUTvX3wkAAKBWlRo4fVPSn0i6WNL/NE3zFkl3SzosaXjymHZlmov3\nSLpRmYbhr0j6con3BgAAVailyVBbs1Gwv5HDIf31fU/P2F7tYVOOwzFjk+F25QVIs73/Vp9XLU3G\nsg4RAACgGpRUUmdZVlzSXklDygRJ75D0T8oETq9O/jks6Ss6FzYNSbp58lwAAFBjDLdLwYC/4L5q\nafy9GF6PS/419XMeN9v7DwY6ciV5AAAAtazkZVIsywpJukjSfkkTyoRKhf6kJX1L0uWWZb1Y+GoA\nAKAWTO1v5JDknDkxaMXZdem6eYdFxfo77dvducyjBAAAqA6OdHrpvmo0TbNZ0rskBSSdp0zJXljS\nC5IetiyrIp1ABwcjK/j71Ori9/s0OBip9DCAkvA5xkq3kj7DdiKpV46NFiyjq3ZOR2ZGVnuzoWDA\nr327O+VyLuy7OjuRzOvvhHNW0ucYKIbPMWoBn2OUwu/3Ff1asdQeTnksyxqT9I2lvCYAAFgZIuNx\nDZyKatPaJvkaPJIkhyOtex86UuGRndPkrZPTkdLY2dScx3YFN2rPlZtLCoum93cCAABYLZY0cAIA\nAKtPfGJCn7v7sI4NRpVKZ2YGbfQ36eN37NBnvtqnE0PjlR6iJOlzv/lWrW9v1P7ekHr7Bmbs93pc\niieSavV5FQx0LGpGEwAAADLmFTiZpnlV9rllWU8V2r4YU68FAABWps/dfVivn4rmXqfS0uunovrd\nzz+m5NwTicrm4f5j6ukO5Poo9YeGNBKJ5QKmW6+9QNHxBOVvAAAAS2C+M5yeVKbpd3raOdntizH9\nWgAAoAyWsq9QtoyukGoKm6RMwLS3a4sMt0s93QHt7doy4+fQYLgrPEoAAIDasJDAp1gjqBpYdwYA\ngNqXTKV04OBR9YcGFR6z1TZLM+z5hFJ2Iqlnjgwt+punchuJxDQatXM9leivBAAAsHzmGzh9ZoHb\nAQBAlTlw8Ghe76LhMTv3uqc7IGn2UGoimdZo1FZTg0f3P/6K+kODGh6zK/Jeimn1GTprJxSLz5xe\n1erzqqXJqMCoAAAAVp95BU6WZRUMloptBwAA1cVOJNUfGiy4b2qpWbFQynrttMZjCYXHbHncDtmJ\n6pvX9LaLz9Md79qmbz/6csGm4MFAB72ZAAAAyoQeSgAArAKjUVvhIrORsqVmLU1G0VBqalPwSodN\nnjqH2pq9itkTGj2TUKvP0A7zXGlgsabg2e0AAABYfgROAACsAi1NhtqajYIlcNlSs9GoXXUlctNd\n3tmu377lEhluV9E+Uy6ns2hTcAAAAJTHvAIn0zTvWI6bW5Z193JcFwAA5KtzOdTgdRcMlIKBDiVT\naX3n8VcqMLL583qc+vDNF+fCo7maftMUHAAAoHLmO8Ppq9KSL0KTlkTgBABAGdz70JG8srisjR0N\nSqfT+sMv/Lhgo+1q8rZL1qvBYHI2AADASrCQ39ocS3zvpb4eAAAowE4k9ZPnThTcdyI8rmND42Ue\n0eJ0X7Gp0kMAAADAPM03cLp+ln1XSfpLSU5Jj0m6S9JTkt6QlJDUJulySXdI+jVJUUm/Ieng4oYM\nAAAWYnBkvOjspVR1T2rKaW/2qq3ZW+lhAAAAYJ7mFThZlvVooe2maa6X9C/KzFb6mGVZf1fgsKik\n1yR91zTNHmXK6O6SdIWk4cUMGgAALIBj5U8qDgY6aPwNAACwgjhLPP9PJbVK+kaRsCmPZVn7JX1F\nUqOkj5d4bwAAoEzJ3KmRcdmJZMH9/jX18npWVlizpskjpyMzs6l75ybt291Z6SEBAABgAUrtvHmz\nFt78+4AyJXU3lHhvAABWtWQqpQMHj6o/NKjwmK22ZkPBgF/7dnfK5Tz3nZLhduntl5yng4ePV3C0\n89fe7NWnfn2nztoTamkymNkEAACwApUaOK2bfFxIaVx2iZzWEu8NAMCqduDgUfX2DeReD4/Z6u0b\nUDKZ0u17tuUdO1FFzZoajDqN2xNF9wcDHfI1eORr8JRxVAAAAFhKpZbUZb8q3b6Ac3ZNPr5e4r0B\nAFi17ERS/aHBgvseffq4vv4jS8lUSslUSnc/8JIee/pkmUdYWKvPkNdT+NcPp0O6fsdGyucAAABq\nQKkznA5JukDSn5qm+Q3LssZmO9g0zc2S/liZMryCjcgBAMDcRqO2wmN2wX2ptPTw4WNKpdKy4xN6\n8oVTZR5dcaejttLpwvvSaWnPlZvzygEBAACwMpX6G90/TD6+WdJjpmleXexA0zRvkvSYpA5JKUmf\nL/HeAACsWi1NhtqajVmPefTp4xULm4rNYmrzGWrzFS6Va2v2qqVp9vcEAACAlaGkGU6WZT1umuYX\nJX1E0qWSnjBN81VJzyjT18khyS/pCmX6PWXXZf59y7KsUu4NAMBqt+38Vj3xfHWUymWtafJo57a1\nSqXTOnjo2Iz9wYBfkvJ6T53b10GDcAAAgBpRakmdJP1XSTFJv/f/s3f38XGXdb7/3zOTuWk6SZo0\nCTdtBXszF6wgpBRFEVtqCt7u4lYtVkDFlbMe3SP78+b8vNnjkZXddXd1f2c9rrvLqiCIRsHl/PTo\nQWoABURtG+7UXmlglbYUmjTTJEOa70xm5vwxN50kM9Okmcx3Jnk9H48+Zr6380k6TMM71/W5svc7\nW9JZ087JBU2jkj5urf3XCrwuAABLTjKV0i33PKmHHjuo4bG42+VMEWjw6LPXv0JNjQElUyl5PR71\n9Q8pOjah1qaQuiLtU/ozlTsGAACA+uZJl2qkMEfGmHMkvU/SGyVFJOV+RZmQ9BtJd0v6mrW26msy\nDw6OVeaLhDo6mjQ4OOZ2GcC88D5GPbtzV3/R0UG1YEvXmbpu2up4TiKpkZijlnBwxuilcsew+PFZ\njMWA9zEWA97HmI+OjiZPqWOVGOEkSbLW7pP0MUkfM8Z4JK2UlLbWHq3UawAAsJSVW5nObWs6w3rX\ntsiM/UG/T52tjUWvKXcMAAAA9a1igVMha21a0tBC3BsAgKUgN/pnWbBBx51JhRv9+uZ9/TpaYmW6\nagv6vYonUmoJB9S1oV07t0VYXQ4AAAB5FQ2cjDGnS9oiaa2kVklftNYeNsaskvRSa+1DlXw9AAAW\nEyeR1PDohHbtPqDH9g8qGkvIIyktqcEnTSbdrlBqDQd10TkduuqytYqNx5kOBwAAgKIqEjgZY06T\n9A+S3i6p8Nebt0s6LOlSSd8yxvRJusFau7cSrwsAwGKQTKXU0zugvv7BGSOYck0IayFsWhEO6L9f\nf7GaGgOSpMbgggyUBgAAwCIw77HvxpiIpD2SdijTKNyjE6vS5Zyd3dcl6WFjzLb5vi4AAPXGSSR1\nJDouJ5Gcsu/WH+7Trt0Ha2a6XCmbzunMh00AAABAOfP61aQxxi/pHklnKvNL2Fsl/VDSd6ad+oCk\nhyS9RlJQmdFO51hr6fMEAFj0CkcwDY86amsO6oIN7fJImX1jcbdLVMvyBkXWtKr/wIhGXowrFMhM\nk4snkmptCqkr0q4dW9e7XCUAAADqxXzHwr9X0jmSJiW91Vr7vyXJGDPlJGvtLyW91hjzEUl/q0x/\np/8s6abZvIgx5pWSPm+t3WKM6ZL0A0n7s4e/Yq3tmefXAQDAgunpHdCu3Qfz20dHHfXuOeRiRTM5\nibRawkHdfMMl+d5MkjQSc+jTBAAAgDmbb+D0NmVGNt2RC5vKsdZ+wRjzKkl/LOnNmkXgZIz5uKRr\nJb2Y3XWRMs3Iv3DKVQMAUEG5FeWKBTPjzqQeeuI5lyqbvYl4Mh+K7eyO5Pd3tja6VRIAAADq2HwD\npwuyj9+bwzV3KBM4RU52YtbT2fNvz25fJMkYY/5ImVFON1prx+bw+gAAVESxqXJdkQ7t2LpePq9X\nTiKpr37/15qIp9wuddb6+oe0ffM6RjQBAABgXuYbOK3IPh6ewzW5X/OGZnOytfZuY8zZBbt+Kenf\nrLV7jDGfkvQZSR8td4/W1kY1NPCDc6V0dDS5XQIwb7yPUQm33PPkjKlyu3YfVCjkl9fj0c+fOqzB\n6HEXK5y76NiEfAG/OtqXu10KlgA+i7EY8D7GYsD7GAthvoHTsKROSR1zuOasgmtPxb9ba4/lnkv6\n0skuiEbHT/GlMF1HR5MGBxlQhvrG+xiV4CSSeuixg0WP/fjR3yk+ma5yRZXR2hRSMp7gvxEsOD6L\nsRjwPsZiwPsY81EurPTO895PZB/fMIdr3jft2rm61xjziuzz10nac4r3AQDglI3EnJKry9Vq2OT1\nSh6PtLI5qDWd4aLndEXamU4HAACAeZvvCKe7JG2TdIMx5jZr7d5yJxtj/l9JVyjTaPyeU3zND0j6\nkjEmIel5STec4n0AADhlPq9HHmX+Qat1Xo+0qiOsj77zQh2fmFRLOKgGnyfbf2pI0bEJtTaF1BVp\n146t690uFwAAAIuAJ50+9R+VjTENyoxUOkfSiKTPSdolqU+Zn8EvVqZn0yXKBEXd2Ut/J+lca23x\nXw1X2ODgWD38/0BdYLglFgPex5iPXKPwPfsGFY05bpdT1qtfdpouPf8Mre4Mq6kxUPSccivsAQuJ\nz2IsBryPsRjwPsZ8dHQ0eUodm9cIJ2vtpDHmDyX9TNJpkv42eygX8Pxq2iUeSaOS3lqtsAkAgErq\n6R2Y0ii8loQCXsUTqSmjlXze8rPng36fOlsbq1QhAAAAlor5TqmTtXbAGHOhpH+R9BZlQqVSfirp\nT6y1A/N9XQAAFtr00T9j43Ht2TfodlkzXHxOh97ZHVFsPC55POpYsYzRSgAAAHDVvAMnSbLWviDp\nKmPMBklvlNQlqT17/2FJT0m611pLg28AQE1zEkkNj05o156DemJgSMOjjlqbAgr4fYodTyh2fNLt\nEqdoDfsVXubXzd/YreFRR23NQXVFOmY1ugkAAABYKPMKnIwxWyUNWGuflSRr7X5J/6MShQEAUE25\n3kx9/YM6Ojq1N1Op1ehqQTSW0P19z+W3j446+Sl/O7sjbpUFAACAJW6+v/r8vKRnjDE3VaIYAADc\n4CSSuvWH+7Rr98EZYVOt85aYyN7XPyQnkaxuMQAAAEDWfKfUrVemZ9NjFagFAICqSqZSuvO+fu3t\nH9TIiwm3y5mhwefRZLL8QqupEoejYxMaiTk0BAcAAIAr5hs4+bOPz8+3EAAAqimZSummW3frwJGY\n26UU5ZE0mUwr2OCVM5kqed6K5QEde3HmlL/WppBawsEFrBAAAAAobb5T6h7OPr55voUAAFBNd/y4\nv2bDJknKDVwqFzatbA6py3QUPdYVaWelOgAAALhmviOcPqhM6PRxY8ykpH+21j53kmsAAHBNMpXS\nHfdZPfjYYbdLkSQ1LWtQSzio8YlJRWOOPCo9TW66rkh7djU6j/r6hxQdm1BrUyi/HwAAAHDLfAOn\nN0q6XdKNkj4l6VPGmEOSDkga1Ylf0BaTtta+aZ6vDwDAnPT0DujBvtoImy552Wl69+vPUdDvk5NI\n6plDI/q7b5dui7giHNDoi3G1r1iml69bmQ2bvNrZHdH2zes0EnPUEg4ysgkAAACum2/g9P9paqjk\nkbQq+wcAANc5iWQ+iJGkvfaIyxVlrOkM631vOlc+b2Z2e9Dv09pVLVrZHCy6Ut7K5pD+23s26bgz\nqXVnr9TQUExHRybyAVPQ76NBOAAAAGrGfAMnKRMyldsuZZYTBgAAmL1cwBTw+/Sd3gHtezaqkVhc\nK8JBbVjTouGxmQ22qyng9+jS887Qzm2RfNiUE/T71BXp0K7dB2dc1xVpV1NjQI2hBt3+w9/q4ccP\naXjUUVtzUF2RjvxoJwAAAKAWzCtwstbyky0AwHVOIqnh0Qnt2n1Ajw8MFQ2VojFHv/ytu6ObAg1e\n/c1/epVWlFk9Ltd7qVRPpp7egSmB1NFRJ7+9szuygNUDAAAAs1eJEU4AALgimUrpzvv61bd/SMdi\n7o5cmo3JZErxRLLsOeV6MjmJpPr6B4te19c/pO2b19G/CQAAADVhzoGTMWatpKslnS9phaQhST+X\n9C1rbbSy5QEAUNy4M6mbb9utw8Pjbpcya61NoXwvqZMp1pNpJOZouEh/J0mKjk1oJObQxwkAAAA1\nYdaBkzHGK+nvJX1I0vRfn+6U9DfGmE9Ya79cwfoAAJgimUqpp3dAP3v8OTmJlNvlzElXpH1eI5Ba\nwkG1lWgqPpcwCwAAAFhoc+nBdIukDysTUnmK/AlL+kdjzCcqXSQAAE4iqSPRcd15X7927T5Yk2HT\nK1/WqdddtEptTQFJJ1bRaGsKqnvT6nwfplOVaypezHzDLAAAAKCSZjXCyRjzaknvVWZluRFJX5b0\nI0lHJHVKerOkP5PUKOmzxphvWmufXZCKAQBLSm5EU1//YNGRPbViZXNQ73n9uQr6fXrblvUaiTla\nFmzQcWdySh+m+dqxdb0alwX08OPPFW0qDgAAANSC2U6pe1f28aikzdba3xYc2y/pYWPMPZIelOSX\n9D5Jn6lYlQCAJWv6qmy1qivSkQ+VCvsvNTUGKvo6Pq9X77/qfL3hFWtmNBUHAAAAasVsp9S9RpnR\nTX8/LWzKs9b+QtIdyswguLQy5QEAljInkdRee8TtMsoKBXwVmS43V7lQi7AJAAAAtWi2I5xWZx9/\ncZLz7pV0vSRzyhUBAJA1EnM0PBZ3u4yi2poCOuesNu3ctkGNQb/b5QAAAAA1ZbaBUzj7OHaS8w5k\nH1ecWjkAAGRGNo3EHGUG19aOS/6gU+/sjlS8LxMAAACw2Mw2cPIr81P/5EnOO559bDzligAAS1Yy\nldKd9/Vrb/+gRl5MqNbynP0HRxXw+yrelwkAAABYbGYbOAEAUFG5UUy5ldwCfq/+9pt9ej56PH9O\nIlndmrweKVVmUFV0bEIjMSffEBwAAABAcQROAICqyAVM4caA7vnZM9prj2h4LH7SkKdagn6vGoM+\nRWOJkue0NoXUEg5WsSoAAACgPhE4AQAWVDKVUk/vQD5gCvg9iidOJEy1EDZJ0ibTqUeeer7sOV2R\ndvo2AQAAALNA4AQAWFB37urX/Xufy28Xhk1u83iktqaQuiLtuuqytdr3bFRHR50Z53k90uauVdqx\ndb0LVQIAAAD1Z66B0yZjTLkV6PI/iRtjLpPkKXcza+1P5/j6AACX5KbEzWV1NieR1IOPPXfyE13Q\n1hTUje+4QB0rluW/nq5Ih3btPjjj3M0XnqlrrzDVLhEAAACoW3MNnG6ZxTm5X10/MIvzGGEFADUu\nNyWur39Qw6OO2pqD6op0aMfW9fJ5vfnzpjcBDzf6dduP9imVcrH4MjaaDq3uCE/ZlxvB1Nc/pOjY\nhFqzo58Y2QQAAADMzVwCn7KjlQAAi1NP78CUUT9HR5389s7uiJKplO68r197+wc18uKJhtuBBo/i\nk7UxfW5NZ1jjE5MnDZF8Xq92dke0ffO6OY/mAgAAAHDCbAOn2xa0CgBATXISSfX1DxY91tc/pKsu\nW6vPf3OvDhyJzTheC2HTyoLRWJPJ9KxDpKDfp87WxipVCQAAACw+swqcrLXvXehCAAC1ZyTmaLhI\nE21JGh6b0L99/zdFwya3vfaC0/XGS86eEi75vCJEAgAAAKqEHkoAgJJawkG1NQeLrtymtPTYwFD1\niyqjNRzURefM7C8FAAAAoLoInAAAJTX4PGoM+YsGTu5PmJtqRTig/379xWpqDLhdCgAAALDk8etf\nAEBJPb0DNTdlrrmxeP+lTed0EjYBAAAANYLACQBQVLmG4W765LWb1L1ptVY2h+T1SCubQ+retLro\nqnMAAAAA3MGUOgBAUeUahrslvKxBna3LtbM7ou2b18161TkAAAAA1UXgBACYYdyZ1F0PPF1TfZrC\nyxr0+Q+8Kr8d9PtYdQ4AAACoUQROAIC8ZCqlb/1kvx7sO6Rkyu1qpECDV+tWtejdrzeESwAAAEAd\nIXACAEjK9Gy6/V6rR5563u1S1NYU0Llntemd2yJqDPJPFQAAAFBv+CkeAJa4ZCqlnt4B7bVHNDwW\nd7WWjZF2bd+8Tm3NIfoyAQAAAHWMwAkAlhgnkdTgseN6cSIhx5lU38BRPfjYc26XpVDAq/e/5WUE\nTQAAAMAiQOAEAEtEMpXSt3+yXw89cVhOogYaNE3z6vPPIGwCAAAAFgkCJwCocU4iqZGYo5ZwcEYg\nM/1YqXOdRFJ33Gv1cA30Z8oJ+j2KJ9JqbQpqo+nQjq3r3S4JAAAAQIUQOAFAjcr1VurrH9TwqKO2\n5qC6IieCmcJjrU0BLV8W0PhEYsq5b9uyVnc98ExN9GfKOb1tmT5x7SYFGrwlgzQAAAAA9Y3ACQBq\nVE/vgHbtPpjfPjrqTNkufD48Fp8SKOXO/fUzwzo8PF6dgss4rXWZ3rF1vdavalFTYyC/v7O10cWq\nAAAAACwUAicAqEFOIqm+/sGix/r6B5VOp2d1n1oIm4J+r85b26aXr1spn9frdjkAAAAAqoDACQBq\n0EjM0fCoU/TY8JijWeZNNcFJpPSTPYfk8Xi0szvidjkAAAAAqoBfNQNADWoJB9XWHCx6rLUpqJbl\n/ipXVF6gIfPPSctyv4L+4v+09PUPyUkkq1kWAAAAAJcwwgkAalDQ71NXpGNKn6ac8YmEJuIpF6qa\nyef1aEvXmXrra9cpNh5XfDKlz3z1l0XPjY5NaCTm0LcJAAAAWAIInACgRuVWo+vrH1J0bEIBv08T\n8WRNhE0eSa/8g05dc6VRYzAz2qox2CAnkVRbc1BHi0wHbG0KqSVcfNQWAAAAgMWFwAkAapTP69XO\n7oje8uqz9R+HR3Xbj36riXhtTEn76NUX6tyz22bsLzcyqyvSrqDfV43yAAAAALiMwAkAalQylVJP\n74D6+gc1POqoVvqE+7werV3VUvL49JFZrU0hdUXa8/sBAAAALH4ETgBQg5xEUrf84Nfaa4fcLmWG\nzReeUXakUm5k1vbN6zQSc9QSDjKyCQAAAFhiCJwAwAVOIlk0jBl3Err9Xqtf/OaIi9VJy4Neveik\nFAr4lE6nFU+k1Noc1MZIx6xHKgX9PhqEAwAAAEsUgRMAVNH0aXJtzUF1RTr0ti1rddcDz+ihJw67\n3qdp44Z2vf8PX5YPxCQxUgkAAADAnBA4AUAV9fQOTGmofXTU0a7dB/Wb3w3ruaFxFys74ZorzYzR\nSYxUAgAAADAXXrcLAIClwkkk1dc/WPRYrYRNazrDWpEd1QQAAAAAp4oRTgCwwHL9mkbHHR0dddwu\np6Q1nWF96rqNbpcBAAAAYBEgcAKABVLYr6mWgyZJamsO6pPXXqRAAz2aAAAAAMwfgRMALAAnkdQd\n91o9/NTzbpcyK8fGHI3EHHo1AQAAAKgIAicAqKDcqKa99oiGx+JulzOD1yOl0jP3tzaF8ivSAQAA\nAMB80TQcAObJSSR1JDouJ5HMr0JXi2GTJK3qCBfd3xVpV9DPdDoAAAAAlcEIJwA4RePOpO6412rf\ns1Edi8XV1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e63cc278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ),\n",
    "    MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,\n",
    "                 batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,\n",
    "                 max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,\n",
    "                 beta_1=0.1, beta_2=0.1, epsilon=0.1)],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds4=model.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds4,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds4)[0],np.sqrt(mean_squared_error(y_test,preds4)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30);\n",
    "plt.xlabel(\"Test target\", fontsize=30);\n",
    "plt.title(\"Scatter plot of [R,GBM,ET,MLP][R] StackNet \", fontsize=30)\n",
    "          \n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds4)))\n",
    "all_names.append(\" [R,GBM,ET,MLP][R] \")          \n",
    "          \n",
    "          \n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:1299: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZftKxo5kuUz3THcKsfwlWD6gu/o/Xqp7l4YSW7/RMB+sIZRdqFU4r4wgw3Zm9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\n+dMSQvgV4M/JfU3j1QJLgHOA1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e48d9668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Read data\n",
    "def keras_model():\n",
    "    # Here's a Deep Dumb MLP (DDMLP)\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128, input_dim=10))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    # we'll use categorical xent for the loss, and RMSprop as the optimizer\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    return model\n",
    "\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ),\n",
    "    MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,beta_1=0.1, beta_2=0.1, epsilon=0.1),\n",
    "    KerasRegressor(build_fn=keras_model, epochs=10, batch_size=15, verbose=0),\n",
    "    \n",
    "    ],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds5=model.predict(X_test)\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds5,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds5)[0],np.sqrt(mean_squared_error(y_test,preds5)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30)\n",
    "plt.xlabel(\"Test target\", fontsize=30)\n",
    "plt.title(\"Scatter plot of [R,GBM,ET,MLP,Keras][R] StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds5)))\n",
    "all_names.append(\" [R,GBM,ET,MLP,Keras][R] \")  \n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
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ZFKtP8h7+9OrzYwg1KSykhdLWc8TVH8ZOTAC8JCIzjTHLQtSxC3sdnnAEnvFP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\n32vWqhULJl1e96H3nKD+IxG1tHbJHwyryluqBl+tPvSeE/j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5HW9gIK5YfJD+SwBmBAIn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kmSpBVs0YFTCKED2AeczYu70QHT2kFcMH6uE7g/hPBTMcavL3ZsSZL0YsBUW5Pk9rue4okf9tI3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\npFBHR0QQDhUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e5edb278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.decomposition.pca import PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Read data\n",
    "def keras_model():\n",
    "    # Here's a Deep Dumb MLP (DDMLP)\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128, input_dim=10))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    # we'll use categorical xent for the loss, and RMSprop as the optimizer\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    return model\n",
    "\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ),\n",
    "    MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,beta_1=0.1, beta_2=0.1, epsilon=0.1),\n",
    "    KerasRegressor(build_fn=keras_model, epochs=10, batch_size=15, verbose=0),\n",
    "    PCA(n_components=1, random_state=1)\n",
    "    \n",
    "    ],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds6=model.predict(X_test)\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds6,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds6)[0],np.sqrt(mean_squared_error(y_test,preds6)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30)\n",
    "plt.xlabel(\"Test target\", fontsize=30)\n",
    "plt.title(\"Scatter plot of [R,GBM,ET,MLP,Keras,PCA][R] StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds6)))\n",
    "all_names.append(\" [R,GBM,ET,MLP,Keras,PCA][R] \") \n",
    "\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
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Nr74okg5IMSM9lLaeI65+F3ZiAuBxEZlrjFkeoo6d2Ouw3GF4xo8ALnaWl2Lf\na9+YoOxkInJltIocj+MpwBRHK+YUbNKNy7GehqWwOjJzTOFlWpyPP3z2HBFJjNUjRUTKYEPQymND\nnWrhvCNEpC3+MPA5RE9cURe/wP7hFA/PhbFZeG/AbxQeISJfhRPPPgx8jOOlLiJVY+mnGWN2iMhr\nwL3OR8dgxfCjIiLVsM+vithQ27FYDaUVwb+JiITy/gP/+75KPNdUCNZjM+FuE5Hbsb+JayQ68xCu\nkVHYLKzHYydUhkQom4Lfk6tkEetnKsoRiXo4KUrRxitCHY8Y9D7P8kbP8h+e5ZPC7SwiiSKyWkSW\niMhI5+Mb8RupbzHGPBlmEFWPgnu2rMHvqdQ+UkEAERkuIjeKyOEQJo0mku3OVmYQQePAgzebSrtI\nBR2hYtfAkFfx61w4ei5ufW1DzGgH4/1N8q0d+Y3Tab0e/31yKpGF8CPVtQ6b0RGgudiU5IWN19Ns\nX9hSHpxwOVfE9RJnBtg1mHxojDmYj+0rdjj6TO7AxBXNds+feocBxpiJWK0osIPIqWFEdl2Nl2Od\nMJ+wiEhvERkqIhc7oTDx4mrO7cOKE88INjY51A/xmduGuiLSxfEIBGwojzHmR2PMo8aYkwBXU6cC\neQyfzQ+c+/wdZ7UO/ms0Fq7FH274ZZAhZIBnebwx5r0of8/if0c0JnJijwLH2Ky+rpdiEvCq9/c8\nzHj7fd3j2C9cvy8affFPmD1ujHnAGPNHCGNTCfx6UcG4fcvSBGqFBiAi5URks4j8LCK3hSiyxtV3\nc36TT53PO+G/V+PG6csMxO95/Rj+PlNw2Wz836d1tOtARK4XkVtEpKfYjH+KogShBidFKdp8ip1J\nBLhORJpGKuzBzbCRBXzv+Xy2Z7lfhP1Pw+oBnYx/8OqN6Y8ktHy1ZzmUF2W0ma9IWhkHATfd7oki\n0ilcWcdFfAzwEv4QqoIk7IBHRKrjz+LydZgBTTDf4P/tr49i7BnsWQ4OKznU0DbXs6Um/mxluRCR\nKth05mDTKxeWYHhMGGP+wT8bDFaEP64wBA/D8WfiekpEWkcq7EVEGhJn+EOU+kphMxa5RBJoDcYd\ncNXDatm416waTGLDPX9dnGugNTY8a3r4XY46rsfvDdESqz0TzJee5bCDTMfbYiLwFNaIElcIsIhU\nxq8XtTac15HjreT1cCrh2XY3dnD/NZGNJl7B/cLOZDUWf1j3U9GMegAiciw2g5vLaM+2UvhDDnfj\nNypG4zU6rmIfAAAgAElEQVTP8o0x7lOQDMF/bTYj8P1wOPFmgx0RwasoGLff9w82JNRLpH5ArH27\ni7AGU8jdt4u1b9kN6xl3CqGzLAczBL9EwxgRCWv4jYYxZhF+r7rywO0Rin/pKdc/XCGnPS8Cz2H7\nmt6JmSIpK6AohYEanBSlCOPoC7jpecsBs0TkjEj7iEh/wNXHmOoMrN36FmLFEwEGSogU1c5szgvO\naiY2RABgu6dYSG8rEelBYEhRqJkhd2AerhPlFSYOVeZJz/IUpyMc3I6a2IGIy7NhjpWfVAYmBBuG\nnM74a9jfD+zgKCrOb/+Ws3oSVtA0F473ltsx3kZgJx6in89oPItfn2p8KC0F55p5E3sOAJ6OIaNd\nUeBF/B3sMgSKPceMsemXb3BWy2HFaG+RCCmYRaSME2K0BL/3RH6ElDyKfxD9bZyhXF/iF1h+Evt7\nbsevCadExjUslcQOPgDme5I2HPUYYzYRaEQaIjYNupcp+Af+w0Qkl5eH44H3Mn6R3ikxGvK97MP/\nfBQRaRTiOCWwg0mvEdn7XvvUs/xoKK1Fp62uQSaHyIP6AscY8zf+93QdYG4krTcRaYl9BrheMBOM\nMV7tuos8296PI7nAG/gH5b2c93ah4XjW3Of56L5D0Q46hHZ8gQ1pBhsC9nmoa9NFREqIyP+wOk5g\nvZSCjR2R+l2x9O3aEBgmGdy3mwmsdZbvDqU/JTarstt/248VPo+I0391NbXK4w/JzSsj8Cf2KBmh\n3HP4jUdPhLo/HO/MN/Eb38YHeYUdat9LUYoNquGkKEWf+7DZRy7GugDPE5GvsWE8BquhUBkrstgb\nf6djGVboMZjrscKhpYFPRGSSU9d+bArou/FnOxnjyarzLnCVszzaCeNydV8aOse+hMBZ5kohju/q\nF1UTkfuwHjkHjDF/BG0HuEtEdmK9rL53BDLniMiLwE1YV/xfReRp4Ftnn7bYTCvurO2HxpiPQrSj\nIOgL1BeRZ7CzjM2ctriDlTeMMfGkgL4LK+zaELjT6cRNwLrcV8VeE9dhn+U5wH9C6D14z+eDIjIO\nSHSMj1FxMnANx6YXrg38LCLPYWc0U7HaJHdgBUUBvsO6qxd5HL2jwcAi7ATM+SLSx3HlB3weSO5s\n8T/GmIZh6nrdMTZOwN5bzwEPiMjbWOHV9c4xjsV6QvQiUPfrTwI91XIhIqHCNhPwhzH0x+9lkQ4M\njVRfiO+QLiIzsff5ac7H78erbSUiA7CZyABeM8YMiFC8dpjvFYmdTihjviMiP2Nn3wFONcb8HKm8\nF2PMXyLyK9ZA7J6/Q/EOK+UMJGNhtjNQLRBE5E9synKAFsaYPIfMGmOmicjF+L2GJotIK2PMZmf7\nPhEZiPX0KIl9T70GvI81RDXFej64ocYbgAfy0I5MEfkA+9wuCXwjImOx786S2N/xeux70UslTx0r\nROQt7D1zMrDcef7/js0e2QgYhM3QB/B2cIas/Dy3cfA41rPleuz5/Nm59z/CPouyse+dnljPVXdw\n/jm5nysDPMtTiRFjzEYRmQWch188PE/ZyPKRSU472mONKi9gtf4ON5dhjU4tsOFkf4jNJPoZtm+x\nHzux0A577TV29nuH0PpZbj+gnIj8H9ZQmmaM+Q17X43E9rOucybJ3sVOYNXFerr2I9DIVE5ESrjv\nBicb3EBsv6AM8K2IjMfqjh7E3hvDsN6zAMPj0DF72vmOrYHuInKNMSZ4Yi0mjDEHnLD3r6OU+8fx\nXnwG27+e7/Q7P8fKOpxAYL9nKbknE/c4ZcsCfUTkM6z3ty9sUFGOFtTgpChFHOdFfgXW8DQc+zJ3\nUy6HYypwe6gZX2PMryJyHraTUR072xwqbOFZPN5KxpiPRWQi1pOjJNaQcmeI/aZgZ517AQ1FpGyQ\niOwH+LPSjHb+5uHXtfgJO4CohzW2zHc+b4R/4H8rtjM/1DnWQyHa4R7r6jDb8puPsJ2P0wkMaXJ5\nBWskixljzE4R6ezU3Ro7aAnl4bYN6G+M+TLEtlnYmfzyWPHay4GDIlLe0TWIpR1Pik1xPRbrUn8/\nocMU3wIGHyHeTQAYY352OpKuiOjTIvJlHrwlMMZMEZFF2Fnc7lgD3R1RdluFvdcmxPB7hMsQFIx7\nPeRF7HQ6fsMyFHw43Y3EH07zGoGD3KLEdPz6eNnY7Gd5pSTW6BwLacSQVKIIMQQ4EzugrQG8LiLn\nuR4CxpgZzntvMvbZNdD5C8Zgs+LtyGM77sBOUjTFehqG8nLch33XPI8ddAcboG7C3uvnYN9TzxCa\nT7AGnkLHOc+DROQ34GGsYPRFzl8oMrHhj//16rmJSG2swQjgX2BunE2Z4tl/kIg8Xlji4WDPi4jc\nhPUET8IKq19tjHnzMLdju/PufwL4D/a660N4za0M7Pt5VJjz9wH+/sdI5+9HoJ1jNB2G/X0TsBMX\nocLIZmEnu252yrUAfKL/xphvRORSrOdaBeAe589LNvB/xpiYvYmdPvAN+CeGnhSRL4wxW2KtI6i+\nOc5Ea0S9RWPMs2IT2P4PO6lzB6Hf5wuBXsGefc619CH2fVoTvzfkzUQX1VeUYoWG1CnKEYAxJsMY\n8xB2Futm/N5NO7CzR1uxg9H/YWflrzLGbI9Q3zzs7OYD2E7HbqeejdgB5pnGmNuDOy7GmBuxs52z\nnGNnYTvjfwKvA2cYY67FuleDHTBdElTHJ9jUw0uxsz/78GhaGGNSsTOKH2NnszOwBqhjPWWyjDGu\n59BLzvH3eb7D+8AFxpjLnPoOB1uwA5dHgJXYAeA6bEfvbGPM9SYPwsuOJ8ep2A7gJ9iZygxgM1af\n63ZAwhib3BCWrtgZvRSs58u/RBDBDVPPU9hZ+KewukB7sb+fwQ4aOhljrjbGxCRSXcR4AHs+wQ6A\nR0coGxFjzApjzPnYQelIbCjKJux5T8N6Os3H6oudAzQzxjwXq/EvBNnYa38N9vq4BWga7nqIgS+x\nvy3Ya/rbCGWV3Hj1mua5XjtKII53w3Wej7oRZFxzPA0bYe+jRdj3Qabzfy72Wj/ZCWnNazvc5/ZD\nwK9YzxH3GIuwz/PmxphXsN6bAOc64UFuHXud9l+JfTevx97vqdgwo6nY99GFh/F9FBPGmGewGnK3\nYp8fa7HP9UzsO34+1iDV2BhzX4h32NX4J6/fCRHKFY2PsP0PKALi4QCOof45z0fjHI3Cw92ObY53\naEvsBM8cbOa5FPz9ovnONjHG/Dfce8QYMxvrpbQYf7/LK3L/JDaz8YfYd2Em9vpdg+1PXYQ1DHo9\n2C4PcZwZ2L7lKGyfdI/T1n+wEwWnGmNG5eFc/Iz/N6lKHsPfPdxDDMLqxorbN8X2rZdiz30mts/9\nBdYY2MlYCYRQ3IidUNqAPQ/biJ7RWFGKHQk5OYU2kaAoinJEExRuNcEYEzEkSsk7IjIFa6gEqHO4\nB/JOuElDY8yJh/O4RzIisherrXNr1MJFCBG5BmtEbW6MMYXcnCKDiAwBxgP1jDHxZMFSoqDnNjSe\ncMOAcGYRaQ+4OlIPGWNGHuZ2vYTfM/M4E59WnqIoylGFejgpiqIoSnROwC82qkRBRBpgQ6GOxHPW\nEustqWLfgbTEekfkKZRFiYieW0VRFKVYogYnRVEURYmAIx56HP6sgUoEnOw947BhRYeiYXTYcQTM\nBwEfGWP2Ryt/tOB4lPQHpsUrIq9ERs+toiiKUpxR0XBFURTlSOMER6wWwBSkLopznAeBp4wx7xbU\ncYoZXbDptQcYY9YXdmPiZDRWJyUugf+jgMex2duiieAr8aPn1kFEEvCL7kNgZrRweDNdbjfGFIhn\noog0Bco5q9UK4hiKoijFETU4KYqiKEcasz3LrbFingWCMWaziDSOJMKvBGKM+UxEGhyh5+wqYM+R\nlGnxMHEJsLMwM4gVY/Tc+kkm9mycLt5MlxOAgtJSfANoV0B1K4qiFFvU4KQoiqIoEThCDSeFypF6\nzowxuwq7DUURY8yOwm5DcUXPraIoilKcOSqy1G3btrf4f8nDRJUqZdm160BhN0NRDgm9jpUjHb2G\nleKAXsdKcUCvY6U4oNexcijUqFEhIdw2FQ1X4qJEiaTCboKiHDJ6HStHOnoNK8UBvY6V4oBex0px\nQK9jpaBQg5OiKIqiKIqiKIqiKIqSr6jBSVEURVEURVEURVEURclX1OCkKIqiKIqiKIqiKIqi5Ctq\ncFIURVEURVEURVEURVHyFTU4KYqiKIqiKIqiKIqiKPmKGpwURVEURVEURVEURVGUfEUNToqiKIqi\nKIqiKIqiKEq+ogYnRVEURVEURVEURVEUJV9Rg5OiKIqiKIqiKIqiKIqSr6jBSVEURVEURVEURVEU\nRclX1OCkKIqiKIqiKIqiKIqi5CtqcFIURVEURVEURVEURVHyFTU4KYqiKIqiKIqiKIqiKPmKGpwU\nRVEURVEURVEURVGUfEUNToqiKIqiKIqiKIqiKEq+ogYnRVEURVEURVEURVEUJV9Rg5OiKIqiKIqi\nKIqiKIqSr6jBSVEURVEURVEURVEURclX1OCkKIqiKIqiKIqiKIqi5CtqcFIURVEURVEURVEURTlM\npB/MYuuuA6QfzCrsphQoJQq7AYqiKIqiKIqiKIqiKMWdrOxsps1ZxZKV29i5J52qFZNp3awGV3Rp\nQlJi8fMHUoOToiiKoiiKoiiKoihKATNtzipm/7zBt75jT7pvvV/XZoXVrAKj+JnQFEVRFEVRFEVR\nFEVRigjpB7PYsG0fi83WkNuXrNxeLMPr1MNJURRFURRFURRFURQlnwkOocsJU27X3jRS9qVTs0rZ\nw9q+gkYNToqiKIqiKIqiKIqiKPlMcAhdOKpUKE2l8smHoUWHFzU4KUcEjz46ks8//yRquaSkJMqW\nLUfNmjURaUHPnr1o1erkw9BCyMzMZMaMD5g9+wvWrFnNwYOZ1KhRg1NPbUefPn1p0KDhIR9j584d\nTJs2lQULvmfTpn/Jzs6mXr1j6djxDPr0uZKqVatFrWPJkl/46KP3WLbsV3bt2knZsuUQaU737hfQ\nrVt3EqOI1WVkZDBjxgfMmTOLtWv/JjX1ADVq1KJNm1Po3ftKmjbNW+zx/PnfMXz4HQA8++xLtGnT\nNk/1KIXH8uW/8u67b7N8+a/s3r2LSpUq0bhxM3r27EWXLl0L5JivvfYKkya9SK9el3LPPffHtM+K\nFb8zY8YHLF78Mzt2bCcpqQT16zegc+cuXHbZ5ZQtG3lmad68uXzyyQz+/PMP9uxJoUqVqjRp0pTu\n3S+gS5duJCQkRG3Dtm1bef/9d1mwYD5btmwiI+MgtWrVol27Dlx55dXUrl0npu+iKIqiKIqiFE3S\nD2axZOW2mMq2blad5JJJBdyiw09CTk44p67iw7Zte4v/lzxM1KhRgW3b9h7248ZqcApF795XMHTo\nPfncokBSUnZz9923sWLFHyG3lyqVzD333Mf55/fM8zG+/34eDz/8IAcO7A+5vVy5cjz88BjatesQ\ncntmZibjxo1l5swPwx7jxBNbMWbMk1SqVDnk9nXr/mH48DtYv35dyO2JiYlcd92NXHPNdVG+TSB7\n9+6lf//L2b7dPpAL2uBUWNdxcWby5Im8+uokwr1TzjjjLB56aDSlSpXKt2OuWPE7Q4YMIiMjIyaD\nU05ODs8//wzTpr0Vtp316h3LuHHPccwx9XJtS09PZ+TIB/juu7lhj3HyyW145JExVKlSNWyZWbO+\n4PHHR5OaeiDk9rJlyzFy5KN07NgpbB16DSvFAb2OleKAXsdKcUCv44Jh664D3DdhYdgwugSgasXS\ntG5W/YjOUlejRoWws63q4aQccQwfPoLmzVuE3JaRcZAtWzYzf/63fPXVF+Tk5PDee9OoW7cel1/e\nt0Dak52dzQMPDPMZm84+uys9elxI+fLlWbZsKW+88Sr79u1jzJhHqFWrdp4MKYsX/8wDD9xDVpYV\nkjvjjM706HEhVatW5++/V/P222/wzz9rGTZsKKNGjeWMM87KVcf//vcYn3wyA4AyZcpyxRX9aNv2\nNHJycli0aAHTp7/N8uXLGDx4IBMnvkaFChUC9t+5cwe33TbYZxRq0qQZl1/elwYNjmP79m18/PGH\nLFr0A5Mmvcj+/fu4+ebbY/5+zz33pK9e5chj5syPmDx5ImANNv37X0vDho3YvHkT06a9xR9//MZ3\n381l3Lgx3Hfff/PlmGvWrOLuu28jIyMj5n3Gj3+KadOmAlCzZi2uuuo/NG0q7N27lxkzPuCHH75j\nw4b1DBs2lFdfnZrLODZ69EM+Y1PDhsdx5ZVXU79+A7Zt28ann87gxx8XsnTpYu6//x6ee24CJUrk\nfsXOmzeXRx75L9nZ2ZQpU4beva+kbdvTSEhI4LvvvuWDD97lwIH9jBgxnClT3qJ+/YZ5PkeKoiiK\noihK4VGpfDJVKyazY096rm3VKiZze+9W1KhStlh6NrkkjRw5srDbUOAcOJAxsrDbUFwoVy6ZAwdi\nH+DlF999N5dVq1YCcMUV/WjZshXVqlXP9VezZk2OO64RnTt34bjjGjF37tcAGLOCyy67IuQA8FD5\n7LOZvPfeOwD07duf4cNHcOyx9alVqzatWp3MGWecxezZX5KWlooxK+jV67KYQm5cMjMzGTr0Zvbs\n2QPAzTffzh133EODBg2pWbMmzZo154ILLmLZsqVs2vQvS5cuplevSyhZ0j9Y/umnRTz33JMAVKlS\nlRdeeJlzzulG7dp1qFOnLm3bnsZpp7Vn1qwv2LlzB6mpB+jQIdC74oknHmP58l8BOPPMs3nqqecR\naUHNmjVp2PA4zjvvfNLSUlm+fBm//bac9u07UqNGzajfb+HCH3j++WcCPjv//J7UqVM35nMUL4V1\nHRdH9uxJYdiwO8jIyKBevfpMnPgaJ57Yipo1a9KoUWPOP78nq1atZN26f/jrLxPzdRGJ77+fx/Dh\nd7B3r38mrnnzFpx++hlh9/ntt2U8/vijADRq1JgXX5xMmzZtqVWrNvXrN6Bbt+5s3ryJv/5aSUrK\nbqpXr0GLFsf79l+8+GdeeMFepy1btmLChCm0aHE8tWrVplGjxpx3Xg+2bdvGypV/snXrFurXb0jj\nxk0C2rBv3z7uuutWDhw4QLly5XjqqRc4//ye1K17DHXq1KV9+47Url2H776bS1ZWFrt27eLss0OH\nIuo1rBQH9DpWigN6HSvFAb2OC4YSSYlsT0ljzb97cm07/cQ6nH5iXUokHZleTV7KlUt+KNy2I//b\nKUoYzj67K506nQnA7t27+eWXnwrkONOmvQVA1arVuP76G3Ntb9CgIQMHDgJgzZrVLFz4Q1z1z58/\nj02b/gWsZ1O/fv1zlSldujQPPvgwJUqUYMeO7bzzzlsB212DGMA999zPccc1ylVHixYnMGDA9QDM\nmPEBGzf6xe127drF119/BUCNGjUZMeKhkMa7wYNv5bjjGpGTk8OLLz4X9bvt37/PZwSoXDl0GJ9S\ntPn005ns22cNPzfddAsVK1YM2F6iRAmGDXuA0qVLAzB16ht5PtaePXt4+un/cd99d7Fv3z6SkmKf\nDZo8eSI5OTkkJSUxatTjVKlSJVeZW24Z6ruuXWO1i+sdCPYeKlmyZK79b7rpFt/yN9/MyrX9ww+n\ns3PnDgBuu+0uTjihZa4y55/fk2bNmgPWsJaZmRnL11MURVEURVGKIFd0aULXtvWoVrE0iQlQrWJp\nuratxxVdmkTfuRigBielWHPKKaf6ljdsWJ/v9a9fv441a1YDcNZZXUhOLh2yXI8eF/oGx998Mzuu\nY3gNZX36hA8LrFWrNm3bngbAnDn+wW5OTg5LliwGoE6dupx55llh6+jR40IAsrKyAgbcS5f+4gvn\n69mzV1hR5cTERLp3v8DZZzE7dmyP9NUYP/4Ztm7dQr16x9K795URyypFk3nz5gBQvnx5OnXqHLJM\n1arVfB5zCxfOJy0tLe7jLF/+K1deeQnvvfcOOTk5VKtWnf/+d1RM++7cucN3H/XocRH16zcIWa5i\nxUr0738tl1zSh/btTw/YVrfuMZx44kk0bdosl+eSd39Xu2nLls25ts+a9QVgjdCR9Nz69r2aCy+8\nhCuu6MeBA6F1nhRFURRFUZSiT1JiIv26NmPUoHaMvqE9owa1o1/XZkesXlO8qIaTUqzJzs72LWdm\nHgzYdsstN7B06eK467z//v/zGWbcEDOA1q1PCbtP2bLlaNKkGcasiNvTavNm/8A1lEeEl4YNG7Fw\n4Q/8889a9u7dS4UKFdizJ8UnNN6ixQkR969atRqVKlUiJSWF335bHrINxx8fvQ1gDV1//PFbSD0p\nsGF+M2d+SEJCAsOGPcCff66IWG9+0bv3hWzevIk+ffrSv/8AnnrqCRYtWkBOTg516tTh6quv5dxz\nu/uuj7PO6sKoUY+zbNlS3n13KsuXL2Pv3r1Uq1ad00/vxNVXX0v16tUB2LhxA2+//QaLFi1g+/Zt\nlCtXnlatTuY//7mW5s2PD9mePXv28NFH7/HDD9+zdu0a0tLSqFChIg0aNKR9+4706nVZLj0tLzk5\nOcyZM4tZs77gzz9XkJKym7Jly9KgwXF06tSZiy++LKSB8LPPZjJ6dFjv17CcfHIbxo+3ek2ZmZk+\n7bJWrU6O6HF08smt+eab2aSlpfH778sDjMGxsH79OvbsSSEhIYHu3S/g1lvvZP/+fTHt+9NPi3wG\n03PO6Rax7HXX5fZSBLj++sFcf/3giPvu37+PvXuty3S1atUDtm3dusVjnD4nYjbIbt26061b94jH\nUhRFURRFUfKf9INZpOxLp0xyCVLTM6lUPjmXxpJbJtS2cCSXTKJmlciZkIsjanBSijVLly7xLReE\n+O7atX/7luvVqx+x7DHH1MOYFWzduoXU1FTKlCkT0zFcQ1lSUlJYDyoXNxwoJyeHDRvW0aLFCRw8\n6A/JiZbu3VuHNxOd11hXtmy5mPYPrsPLgQMHfKF0F110CW3atD1sBieX/fv3MWTIoIA2rlmzmho1\nauQq+/rrk5k06cWAzGabNm3kvfemMW/eXCZMeJWVKw0PPTQiIIvg7t27mDfvGxYs+J4xY57MlUFw\n1aq/uOuuW3N5gu3atZNdu3aydOlipk59g8cff4qWLVvlateuXTu5//57AgyfACkpKSxbttRnJBs1\namzI/Q+VDRvW+0K+6tU7NmLZunX9Wd/Wrv07boNTQkICHTqczsCBN/gMp7EanFavXuVb9hr+MjMz\n2bZtK1lZWdSsWeuQM+hNnjzRdz66dAk0bHnb4NWGysnJYefOHezbt4/q1atTrlz5Q2qDoiiKoiiK\nEj9Z2dlMm7OKJSu3sWNPOokJkJ0DVSuUoo3U9IXAuWV27kmnasVkWjercURnmCto1OCkFFt++mkR\n8+fPA6w+kBtu5nLvvQ+GTUseiVq1avuWvZnVvJ+HombNWr7lbdu2hg3rCaZSJattlJWVxY4d23N5\nTnjZunWLb3nHDqsVU7FiRRISEsjJyWHr1q0Rj5Wensbu3bsBfFoz3jbYtm/JtV+0NgTz4ovPsWnT\nv9SsWYubb74tYn0FxRdffEp2djY9e/aie/cL2LdvHz//vCiXp9rSpYuZO3cONWrUpG/f/jRv3oId\nO7bz+uuT+euvlWzduoWHH36QP/74jVKlkrnhhps5+eQ2ZGRk8OmnHzNr1hccPHiQcePG8M47H/o8\nW7KyshgxYjg7dmynTJky9O3bn5NOak3ZsmXZsWM7c+bM5quvPmfPnhQefPBe3nnngwCDY2pqKrfe\nOpi1a9eQkJDAued2p3Pnc6hRowYpKSksXDifjz/+iO3bt3HHHbcwYcKrNGrU2Ld/p05n8uqrgVpf\nsVCmjN9ouW2b/3qKdv3XquW//vOSkfC883pEDEOLhGsYLl++AuXLl2fTpn95+eWXmDfvG1JTUwFI\nTk6mU6czueGGIRxzTL1I1fnIzs5m586dGLOCd9+d6vNe7NDh9FweSl7jdK1adUhNTeX11yfz+eef\n+M5HYmIiJ554EgMH3hC3QU5RFEVRFEWJD6+n0vvfrmb2z34N22xnnnnn3oyAz73LO/ak+9b7dW12\neBp9hKEGJ6XYkJWVxf79+9iwYT3z5s3l3Xen+sJohgwZ6hMtdonmkRELe/ak+JajeQ95PZpckeVY\nOP74lj7tl3nz5nLJJb1DlsvIyODHHxf61tPS7EC6VKlSNG3ajJUrDcuWLSElZXeAAcnLwoULfOfM\n3d9tg8u8eXPp2vW8sO11jXzBdbgsWfILH330HgB3331foXl0ZGdn061bd+6990HfZ67IvJfdu23G\nsokTpwRkV2vTpi2XXnoB6enpLFnyC+XLV2DChFcDDIlt257GwYMZzJ07h3//3cjq1ato2tS+jJYt\nW8qGDda76p577ufcc88POG6nTp2pXr06U6e+wbZtW1mwYD5nnXWOb/vEiS+wdu0akpKSGD36f7ky\ntLVv35Hu3S/glltuIDX1AGPGPMLEiVN82ytWrETFipXycOb8uJkTIbrnW+nS/uvfm10uViKFoEUj\nJcUaUStUqMBPPy3k/vuH5TI2p6en8/XXs1iw4AceffRxTj21XdR677jjFn755ceANvbr9x+uvXZQ\nLlF9tw0AqakHGDCgb4AwP9hr8tdflzB06M3ceOMQrr56QLxfVVEURVEURYmC15vJ9VTan3Yw4j6L\nzTbCJRpfsnI7l3VuHHN43dGE+n0VA9IPZrF11wHSD2YVdlMOC7fdNphOndrm+uvcuR09epzDDTcM\n4M03p5CRkUFycjJ33XVvnj0jonHwoD/cLVTWNi+lSiXn2i8Wzj67qy/U55VXJvDvvxtDlnv55RfZ\nvXuXb92b3eq883oAkJaWxrhxYwO0rVz27t0bkFnOu3+TJk1p0sQaSr75Zjbffz8v1/4A8+d/x/z5\n34Wswz3+mDGPkJOTQ7du3enYsVPoL32YuPji0Ma7YK6++poAYxNYry+vN1SfPleG9FrzCmlv3OgX\nrndVo1oAACAASURBVPd6kIUzfvbp05cLL7yEG2+8hWOO8ZfZu3cvM2d+CMCFF16Sy9jk0rz58fTr\n9x8A/vjjN37//bew3zEvHDzoT58bLRwtOdl7/R/etLuucWnv3r088MBwMjLSueaa65g27SO++WYB\nb7/9AVdeeTUJCQkcOLCfESOGxZRkYMuWTQHr2dnZfP/9PJ+BOFQbAB56aAQbN27grLPO4ZVX3mTO\nnB/4+OMvufPO4ZQvX56cnBxeemk8X3+dO9OdoiiKoiiKcmhMm7OK2T9vYMeedHKwnkppGbnHR152\n7U1n5570MNvSSNkXetvRjhqcjmCysrOZOnslIyYt5L4JCxkxaSFTZ68kK4Qx4WiiVKlStGhxAtde\nO4i33/4grEdQfpB3r4sw5vEQVK9e3efpsHv3LgYPHsjMmR+xa9dODh48yF9/reSRRx5k6tQ3Aowi\n3rTtF198mU/Me86cWdxxxy0sXbqY9PQ09u/fx7x5c7nxxgFs2LDOV0eJEoFp32+99Q4SExPJyclh\nxIhhTJr0Ihs3biAzM5PNmzcxZcrLjBgxjCpVqvrEo4NTx0+Y8DwbN26gcuUq3H773bGfrgIgKSmJ\n5s1bxFS2bdvQ3i7e8x0csuniZi0DfOFbEKgpNnr0w/zyy0+5DIE1atRk+PAH6N9/gM8zCqyXmJvp\nLZonTocO/mxrXm+c/CAx0T+LkxBuyicE8ZTND9xztW/fXlJTD/Dww2MYNOgmjjmmHiVLluTYY+tz\nyy1DueOOYQDs37+fCROej1rvwIE38tJLkxk/fiLXXz+YSpUqsXbtGsaMeYQXXngmZBvAZrC7/PK+\njBo1FpHmlCpViqpVq3HppX145pmXfMbp559/Oi7jtKIoiqIoihKZ9INZLFkZv7xDlQrJVK2YHGZb\naSqVD73taEdD6o5gXMusy9ESQzp8+IgAQ0FqaiorVvzO1Kmvs2PHDkqVKkW3bt3p0+fKiAPbDRvW\n51nDyQ1FcvVssrKyyMrKipilKyPDb/VOTo5PnHjAgOvZunULn3wyg507dzB27CjGjg0s06xZc665\n5joeeOAeIDCEKTm5NGPHPsmdd97Cxo0b+OWXH3MZHxISErj22kFs2bKZzz6bSZkygSGIp5xyKsOG\n3c8TTzxGZmYmr732Cq+99kpAmcqVq/DYY+O46aaBudqwbNlS3n9/GgBDh95N5cqhw/oOF5UrVw7w\nuolEnTp1Qn7uNaiF09bylvGKjjdt2oz27TuycOEPrF27httvv4lKlSpxyimn0bbtaZx2Wntq1w59\n3L/+Mr5l9/eOBa933J49KWzZsjlC6dCUKVPW55FVtqz/9/Ve36FIT/dvP1Rx7njx/s5nnnkWnTuf\nHbLcpZf24eOPP2TVqpV8993cqOL+557r12k6+eQ2dO/ekyFDrmfLls1MnfoG7dp19GkxedtQrVp1\nbroptHaZSHN69bqU6dPfZuvWLSxZ8gunndY+ru+rKIqiKIqiBOLqNWVkZof1VIpEG7GJhbzjb5fW\nzaprOF0Y1OB0hBLJMlvcY0iPOaYeTZtKwGetWp3MOeecx2233ci6df/w7LPj+Oefv7nnnvvD1jNm\nzCMsXbo47uPff///0aPHhUCgblNaWmpEPSKvd0uFChXjOmZiYiL33vsgbduextSpr7Nypd/gUKdO\nXS666FKuvPIqFiyY7/u8atWqAXUcc0w9Xn75DZ9QsRt+l5CQQJs2benf/1ratj2N++67C4AqVarl\nakfPnhfTuHFTXn55Ar/88qMvZK58+fJ07dqdgQMHUbJkKZ+njtuG9PR0HnvsYbKzszn99DMiakAd\nLqJpDrnEkh3QLRcvDz00miefHMtXX31BTk4OKSkpzJkzizlzbChV48ZN6Nq1O5dddnnAteYKu8fL\n3r1+zaXvv5/H6NEPxV3HySe3Yfz4iUDgOUxNTQu3CxCo53Wo2lHx4m3nmWeGNja5nH76GaxatZLM\nzExWrvyTk05qHfNxateuzV133cuwYUMB+PTTj30GJ28bOnQ4PZf3X3Abpk9/G7ChkGpwUhRFURRF\nyRvBek1VKpQiuVQSaRm55WhKl0qibHISO/dmeLLUJdNGaviy1IEdb+/am0aVCqVp3ax6wDYlEDU4\nHaGk7IseQ1qzSmQR6+JG9erVGTv2Ka67rj8HDuxnxowPqF27Lv37DyiwY3o9ULZs2UKjRuENTm72\ntoSEBKpXD59pLhJdu55H167nkZKym127dlGpUqWAkK1//lnrW65T55hc+1eoUIEhQ27npptuZevW\nrWRkpFGzZu0AQXW3jrp164ZsQ4sWJzBu3LOkpqaybdtWSpVKpkaNGj6Dy2+/Lfe0wdYxefJE1q9f\nR1JSEr16XRbgoeOyY4ffgLpx4wYqVKgAQMOGjSIOzvNKrGFdeTEkxUq5cuV58MFHuO66wXzzzWx+\n+OF7fv99uc+Qt3r1KlavHs+HH07nuecm+LKnZWX5tbEee+x/YT2hQh0vP/FmpvNmJwzFli3+7Xm9\n/vOK1/ssWIsrGG82ybwY9tq160Dp0qVJS0tj9eq/CqUNiqIoiqIoRwveTHOhHC6Co4J27g2vJdqp\nVR0u69yYlH3plEkuQWp6Zq56+3Vt5isT7piKHzU4HaFUKm9jSHeEMDodzTGkxx5bnzvvHMaoUf8H\nwCuvvMSpp55G8+bH5yrremkcCscd18i3/O+/GwLSzgfjZqSqXbtuTB4zkahUqXLITHN//GGNPTVq\n1IwYspaYmEjt2rnT2O/Zk+ITS3ZFwsNRpkyZkCLZbhsAnyfa77/bz7KysnzeH5EYO3aUb3n69I99\nhqviSt26x3DVVddw1VXXcODAAX79dQmLFi1gzpxZ7Ny5g61bt/D444/yzDMvAoEeQpUrV8nl8RcL\nPXpc6PPUO5R2u8aV4Ixrwfz7r3+7qyd2uGjcuAnz5n0DBHp5hcIraO4aPXNyctiyZQv//ruBChUq\nBuhpBZOUlES5cuVJS0sL0F9q3Ng/8xW9Df793DYoiqIoiqIofkJlmmvdzHoiJTk6u5GigkqXSqJc\n6RLs2pse4KmUlJjoc9yoUDa0DERyyaSjzrkjr6ho+BFKcskkWjerEXLb0R5D2r37Bb6sXZmZmYwe\n/VCubGn5xfHHt/Qt//rr0rDl9u/fx6pVKwE46aST4zrGhg3rmTjxBcaOHRXSM8glNTWVn35aBOQW\nkp4792vGj3+aJ58cG2pXH999960vHM5bR0ZGBq++Oolx48aGzMDlZd68uYD1bgqXfU2x1+a6df+w\nbFngdVO2bFk6dDidoUPv5s03p1O3rvVU++WXn0hPt2FrXsOma8wLx7p1//Daa6/w1Vefs379unz9\nDgkJCbRocQJgNbq8GlXBLF26BHBF/XMbgAsS730a7Xz9/fca37Jr6ExJSaF3757cdttgJk16IeL+\nBw7s94Wr1qjh91Rq1qy5L5Nl9DasztUGRVEURVEUxU+oTHOzf97AtDmrfGUiRQVlHMzi9t6tGH1D\ne0YNake/rs18hiol/9AzegRzRZcmdG1bj2oVS5OYANUqlqZr23oaQwrcc8/9lCtnNVPWrFnNO++8\nWSDHqVOnrs97avbsL8nICO2i+fnnn5CVZeOEo2nIBJORkcHrr09m5syPIqZJf++9ab5MWOed1yNg\n2++//8Y777zJBx9MZ926tSH3z8zM9J2nOnXq0qqV3zBWqlQp3n9/Gh9+OJ333psWtg2//bbcp4vl\nbcP48RP5/vufI/7dfPPtvvLPPvuS7/PiOuC+667b6NfvMoYOHRKg7+WlYsWKtGzZyreenm6vr1NO\nOdUX6vfJJzMiGlRfe+0VJk16kYcffpDffluWj9/ActZZ5wA2g+IPP3wfsszOnTtYsMBua9euwyF7\n+MXLqae283n8ffnl5xw4sD9kudTUVL79dg5gPfzca69y5co0aNAQgB9/XBgxfNB7r596qj9zYfny\n5WnfviMAK1b8wYoVv4et44svPgWst1SHDp1i+YqKoiiKoihHDdH0jNMP2r6YGxUUiioVSlOjSllq\nVil7VDtrFDRqcDqCSUpMpF/XZowa1E4ts0FUr16D66+/ybc+ZcrLbNr0b4Ec67LLLgdg27atjB//\nVK7t//yzlsmTJwFQr96xdOwY3wCyUaPGvtC1jz56j82bN/0/e3ceH3dV73/8PTPJzCSdmaxTShdA\n2s4XESlpyyKIhRoWRVksUqwUBP1dvOpV7hW9sojgvXgVEfV63a5XZbMQ5LJcuXChIQUKyNImtGz9\nprEsXSjZk5km+c5kJr8/kplmmayTzCST1/Px6GMy3+2cyBDLO5/zOUOuqa7eoj/+sXeJ4HHHLU80\nKo5btWp14utf//o/htwfi8X0s5/9OFHdcdllXxzSuyj+jNdffzVRxdRfff37+v73r5fUu+Trwgsv\nHuu3OCudckrv5yActvTb3w79ZyL1BjXx3QQXLFgon6+32XxJSanOOKN3h7S3335LP/3pLUmri6qq\nKhMVaSUlJVq9unzSv48zzjgrscTvZz+7Vc3NTQPOd3d365Zbbk6EoRddtG7S5zCanJycxLhNTY36\n4Q//dUhIF4vFdOutP0j0TDr//DUDzl9wwYWSer+fH/3o5gHL3uK2b39Fv/lN7z9Lr9enc8/9zIDz\nn/vcpYneYTfffKMaGxuHPOPhhx/QCy88L6k3zCsqKhr39wsAADBTBTvCevPtZgU7hu+1NJZ+xhKr\ngqYDejhlAdaQJveZz3xWjz32F9XWmurq6tJtt/1IP/7xzyd9nLPPPkePPPKwtm2r0QMP/Fn79u3V\n+edfqIKCAr366nbdeecfFAoFZbfb9c1vfiexrKa/m2++UY899oikgbvgxV155Vd13XXfVigU0pVX\nfkGXXHK5AoGj1NXVqWeffUb/8z8PKBqNyucr0He+890hzz/mmA/rlFNO1XPPbdbmzU/pqqu+ovPP\nX6PS0rnat2+PHnjgz4nql1NPXaVzzjl3yDPWr79CTzzxf+rs7NCNN16rz372c1q58gTl5OTo1Ve3\n6b77Nqi1tVU2m03f/va1I/aQSsWFF346EbrN5P5On/rU+brvvnu0f/97uv/+Cr311i598pOf1qGH\nzlc4HNauXXW677571NTUG+Bcfvn/G3D/1772j6qu3qL6+vf18MMPaOfOWl1wwYU67LAj1NLSrOee\ne0aPPvoXxWIx2Ww2XX31NVNSWeTzFegrX/kH/fCH/6r33turL33pUl166eVassRQff37qqj4U2IJ\n2VlnfVJlZSuGPKO6eou+/vUvSxq4C95kWrfuUj333Ga9/vqrqqraqN2739GFF16sww//gBoa3tf9\n91do27beZX9lZSt03nkDw6Lzz79QTz65Ua++uk0vvvi8Lr10rT73ufU64ogPqKurS88994z+538e\nVCQSkcPh0PXX35QICOOWLTtOa9d+Xvfee7fefvstffGLn9dnP/s5ffjDyxQOW9q48fHEz4HCwiJd\nddXVk/6/AwAAwHQU7u7WzXdWa29DSLEeyW6TFvg9uu7S5XIO+u+n8fQzjq/+YWe5zCBwQtZyOBy6\n+upr9OUvX6FYLKa//vU5bdpUqdNPn9wqD5vNph/84Mf65je/rh073tALLzyfqFCIy8nJ0dVXXzOk\nt9JYrVq1Wlde+VX953/+Sk1NTfr5z28dcs2hh87XD35w67B9k66//vu6+uqv6/XXX9WWLS9py5aX\nhlzz8Y+fqWuv/V7SHdzmzZunm2++Rddf/8/q6DigP/3pDv3pT3cMuCYvL0/f+ta1iWVWGF5+fr5+\n9KOf6uqrv66Ghnpt3fqytm59ech1DodDX/rSl3X22ecMOF5YWKhf/vJ3uuaaq1VXV6s33nhNb7zx\n2pD7XS6Xrr76Gp166mlT9a3oU586X++//75uv/2/VF//vm699YdDrjn55I/q29++dsrmMJqcnBzd\ndtsvdMMN1+rFF5/Xzp21+rd/+/6Q60444SO66aYfDPl3ICcnR7fc8jN973vX6KWXXtDu3e/qlltu\nHnK/1+vT9dfflOgjN9jXvnaVcnJytGHDnWpqakpURPU3f/4C/fCHtw3YgRIAACCb3XxntXbXhxLv\nYz3S7vqQbr6zWjddccKAa+OVS/13n4sbXLkUXxXEznKZQeCErHb00cfo3HMv0EMP/bck6ec//4lO\nOOGkSd8evqCgUL/5TW+fpY0b/09vvbVLnZ0dKikp1YoVx+viiz+vI49MLUVfv/5ylZWt0J//fI+2\nbXtFLS3NcrvdOvLIxTrttI/rvPPWyO0evoLF6/Xql7/8nf7yl4f0xBOPadeuOnV1damoqFjHHHOs\nzjvvAh1//EkjzuGEE07SnXfeq3vv/ZNefPF5vf/+ftlsNs2fv0Af+cgpWrNmrQ45ZOjud0hu8eIl\nuvvu+/Twww/o+eef1dtv71IwGFReXp78/rk6/vgTde65n9ERR3wg6f2HHjpfv//9XaqsfFybNlVq\nx4431dbWKofDoQULFmrlyhO1Zs1FicbjU+mLX7xSJ574Ed1/f4W2b39Fzc1NcrvzFAgYOuecc3Xm\nmZ9IGmSm05w5Hv3kJ/+uzZuf0qOPPqI333xdbW2tKiws0pFHLtGnPnWuPvax04csJ43zer36yU9+\noWee2aTHHntEb7zxutrb25SXl6/DDjtcJ5/8UV1wwWeHVDYN9uUvf02rV5frwQf/W9XVL6uhoUF5\neW4tWLBI5eVn6ZxzPj3pP6MAAACmq2BHWHsbQknP7W0IKdgRHrJj3Hgrl1gVlBm2kXYVyhYNDcHs\n/ybTxO/3qqEhmOlpYJa755679ctf/kz/+7+VKigY/9I9PseY6fgMIxvwOUY24HOMbJDpz/Gbbzfr\nx/cOv+P3ty4+Th88InnltxWJUrmUYX6/d9jfKtNdGsCM89Zbf9OcOXMmFDYBAAAASD8rElV9S0di\nF7m4hXM9sg8TWdhtveeHE69cImyanlhSB2BG2batRpWVTwxprA4AAABg+onGYqqoqlNNbYOa2y0V\n+1wqC/i1dvUSOex2efOdWuD3DOjhFLfA7xmynA4zx7QPnAzDcEj6nSRDUo+kL0vqknR73/vXJH3V\nNM1YpuYIIH3+4z9+qqOP/pC+8pWvZ3oqAAAAwKyQytK1iqq6AQ2+m9qtxPt15QFJ0nWXLh92lzrM\nXNM+cJL0aUkyTfMUwzBOk3SzJJuk603TfMowjN9IOk/Sg5mbIoB0ufXWf5fPV5DxBtQAAABAthut\nOmk0ViSqmtqGpOdqahu1ZtViuXIdcubk6KYrTlCwI6w99SE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AmGEG92py5tplRSZv+dxg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\nDSUbewe363vW691v2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x169f91fb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.decomposition.pca import PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Read data\n",
    "def keras_model():\n",
    "    # Here's a Deep Dumb MLP (DDMLP)\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128, input_dim=10))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    # we'll use categorical xent for the loss, and RMSprop as the optimizer\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    return model\n",
    "\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=True\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ),\n",
    "    MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,beta_1=0.1, beta_2=0.1, epsilon=0.1),\n",
    "    KerasRegressor(build_fn=keras_model, epochs=10, batch_size=15, verbose=0),\n",
    "    #PCA(n_components=1, random_state=1)\n",
    "    \n",
    "    ],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [ Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds7=model.predict(X_test)\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds7,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds7)[0],np.sqrt(mean_squared_error(y_test,preds7)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30)\n",
    "plt.xlabel(\"Test target\", fontsize=30)\n",
    "plt.title(\"Scatter plot of [R,GBM,ET,MLP,Keras,PCA][R]  StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds7)))\n",
    "all_names.append(\" [R,GBM,ET,MLP,Keras,PCA][R] Restacking \") \n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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x2f27Ew++D+dbpcPi/iNK6fjFEA2044l/QzXCb4yS9K6CPaeLM+OwZMZHARtp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\nIMnefrtCZWXP9mqsDgAAAGDkCYXDWr12myoqa1TXZCk/26USb5GWLpw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ho8IJAAAAAEaI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\nLRs6FItGNJi0a1qLQ1I4aCgWj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x169e5babef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.decomposition.pca import PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Read data\n",
    "def keras_model():\n",
    "    # Here's a Deep Dumb MLP (DDMLP)\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128, input_dim=10))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    # we'll use categorical xent for the loss, and RMSprop as the optimizer\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    return model\n",
    "\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=True\n",
    "RETRAIN=False\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ),\n",
    "    MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,beta_1=0.1, beta_2=0.1, epsilon=0.1),\n",
    "    KerasRegressor(build_fn=keras_model, epochs=10, batch_size=15, verbose=0),\n",
    "    #PCA(n_components=1, random_state=1)\n",
    "    \n",
    "    ],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [ Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds8=model.predict(X_test)\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds8,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds8)[0],np.sqrt(mean_squared_error(y_test,preds8)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30)\n",
    "plt.xlabel(\"Test target\", fontsize=30)\n",
    "plt.title(\"Scatter plot of [R,GBM,ET,MLP,Keras,PCA][R] Restacking No-Retraining StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds8)))\n",
    "all_names.append(\" [R,GBM,ET,MLP,Keras,PCA][R] Restacking No-Retraining\") \n",
    "\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KhFC1ajWaN2/JAw88RFhYWJ6PjxBCCCGEKFjci4QHBwV63K5R7bIEF/O87nJW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\nwuuyitpq2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d6046689e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#export models\n",
    "from sklearn.externals import joblib\n",
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ),\n",
    "    MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,\n",
    "                 batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,\n",
    "                 max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,\n",
    "                 beta_1=0.1, beta_2=0.1, epsilon=0.1)],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "joblib.dump((model), \"mymodel.pkl\")\n",
    "\n",
    "model=joblib.load(\"mymodel.pkl\")\n",
    "\n",
    "pred=model.predict(X_test)\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), pred,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),pred)[0],np.sqrt(mean_squared_error(y_test,pred)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30)\n",
    "plt.xlabel(\"Test target\", fontsize=30)\n",
    "plt.title(\"Scatter plot of pickled model \", fontsize=30)\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "levels of oof  2\n",
      "1st level shape   (4999, 4)\n",
      "2nd level shape   (4999, 1)\n",
      "first 10 rows of 1st level predictions \n",
      "[[11.73312115 13.27490641 13.36034195 11.68794099]\n",
      " [10.04685506 10.81669056 11.83415277 10.10382585]\n",
      " [22.86414165 24.05822401 21.31558284 23.08956115]\n",
      " [13.8865295  14.06498889 13.26990506 13.63735579]\n",
      " [17.51773137 17.37738113 16.9877191  17.50239889]\n",
      " [19.39313789 19.36601861 19.30266164 19.60542476]\n",
      " [15.30518576 15.06773239 14.4239658  14.99237725]\n",
      " [16.31525469 15.6234919  16.31260021 16.09117784]\n",
      " [13.68511969 14.7806224  16.07478195 13.44684724]\n",
      " [ 9.03498573  9.94764644 10.99441609  9.0473319 ]]\n",
      "first 10 rows of 2nd level predictions \n",
      "[[11.88628767]\n",
      " [10.14755639]\n",
      " [23.42902992]\n",
      " [13.65099488]\n",
      " [17.58027875]\n",
      " [19.74028214]\n",
      " [15.00251239]\n",
      " [16.0673105 ]\n",
      " [13.66108332]\n",
      " [ 9.09246234]]\n",
      "(5000, 4)\n",
      "(5000, 1)\n",
      "levels of test predictions  2\n",
      "1st level shape   (5000, 4)\n",
      "2nd level shape   (5000, 1)\n",
      "first 10 rows of 1st level test predictions \n",
      "[[ 9.84843975 10.88740537 11.30159271  9.90091412]\n",
      " [12.1011691  12.3069137  13.29177042 11.86591735]\n",
      " [18.56179104 18.3481833  17.84994877 18.37292667]\n",
      " [15.82440198 15.9019808  15.48420566 15.70754096]\n",
      " [ 4.49060486  6.53153648  7.38120707  5.48157697]\n",
      " [18.7830101  18.17186092 18.12311401 18.70353807]\n",
      " [ 6.89032276  8.94357811 10.29674896  7.47119649]\n",
      " [13.49020292 12.23591483 13.01201404 13.0709618 ]\n",
      " [18.27186123 17.99562667 18.21969173 18.06436194]\n",
      " [19.39799502 18.83573906 18.33142728 19.20681617]]\n",
      "first 10 rows of 2nd level test predictions \n",
      "[[ 9.9776865 ]\n",
      " [11.90020183]\n",
      " [18.45815068]\n",
      " [15.76949033]\n",
      " [ 5.52990282]\n",
      " [18.73452401]\n",
      " [ 7.62024496]\n",
      " [12.9180244 ]\n",
      " [18.15117456]\n",
      " [19.25747997]]\n"
     ]
    }
   ],
   "source": [
    "#export oof and test predictions\n",
    "\n",
    "from sklearn.externals import joblib\n",
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=0\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Ridge(alpha=0.001, normalize=True, random_state=1234),\n",
    "    GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1),    \n",
    "    ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ),\n",
    "    MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,\n",
    "                 batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,\n",
    "                 max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,\n",
    "                 beta_1=0.1, beta_2=0.1, epsilon=0.1)],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "all_oof_preds=model.fit_oof(X,y)\n",
    "print (\"levels of oof \" , len(all_oof_preds))\n",
    "print (\"1st level shape  \" , all_oof_preds[0].shape)\n",
    "print (\"2nd level shape  \" , all_oof_preds[1].shape)\n",
    "\n",
    "print ( \"first 10 rows of 1st level predictions \")\n",
    "print( all_oof_preds[0][:10])\n",
    "\n",
    "print ( \"first 10 rows of 2nd level predictions \")\n",
    "print( all_oof_preds[1][:10])\n",
    "\n",
    "\n",
    "test_pred=model.predict_up_to(X_test)\n",
    "\n",
    "print (\"levels of test predictions \" , len(test_pred))\n",
    "print (\"1st level shape  \" , test_pred[0].shape)\n",
    "print (\"2nd level shape  \" , test_pred[1].shape)\n",
    "\n",
    "print ( \"first 10 rows of 1st level test predictions \")\n",
    "print( test_pred[0][:10])\n",
    "\n",
    "print ( \"first 10 rows of 2nd level test predictions \")\n",
    "print( test_pred[1][:10])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1d60a156ac8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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11dY1ZcEhd3+DyJaByBCat8Y22i3NibQ05YPxkxHn7RHEHEqvmNnmHWhuLiKz6QMqA9zF\n/rxLJcg8BTBT9lkqa7VFenlvIfiTrf8jcW/JVA3ImNmiwG1U5tupO/iTcySVc2ptM9uh3hVTYDUL\n5PUvBH9Gc/d/UDkmu7dVVrBBWZlEqH4fvxk4AziaCK611zeMmRnZ2bJSmD8T1+YXxQXS38qs9OHv\nraS06DjSlfeYcfmdioh0GQWARERERERkYvUd8WT40cCC7n5WHetUKw/3bu73u8sCBGmg6fP0cprC\nx9m8QzuZ2X5pgLa4/tHufpi7X5a9lwbd/pBePlAreykNhGcDZWvmPnqVGCADONXM+hQHRt39W3ff\nxd3/ls+uaYvFfD5ZdtET7v5otWXToO1t6eWyqcxVZigxGAnlZeCy7J9RRBZU1v4MRFksgDvbmFz+\nfqJ0FIx5fIoeqvFZVelY/D69fCBl7YwllcQa2p42OkFWhmqkuz9SbaF0LmeZTwulJ+3L1DpWWVsv\nuPt71RZy95FUnur/XY1B+1qZWPmAQFvzFrWbuz/v7isRwYhjiEnsfypZ9FfAFWZ2eRsl9Kq1s4O7\nzwXMlc6XavKZRJPnfu9NzNEDYwdM8+08Q5Tv6unuh5ctk+YRGkZkJH1G+4I/uPs3ROA1u0ZPbaDc\nZL582rCqS4Wb08/piFJeneUjKnOBHWoxH9yUxYXcfR93H5ACB+31hLt/3/ZijUv3qd7p5X+y7Kgq\nNiVKVs7Txr21K3XlPWZcfqciIl1GJeBERERERKRV7cKY8ydMDSxHlK6aHfiWmKfnjAYGr96s8n4+\nk2OsDJeS5YpPSx8HXE5kCpwKnGhmDwJ3EAOaj7h72UByL+IpZYANU4ZTPUZnQbj7p2Z2LvEU9FxE\nGasvzOzu1P7t7u51brdoHioD7vUETh4i5pHoRpRpuif18T0zG0ZkIWxuZvsUjkcWAHrA3fMToS9B\n5VjvbWb1llabr8Znb9e5jaI5iUFnqMw7UU3V4EsXy+bv6NnAuQRxvMoGWEuPVRpkXjS9XKyBtqYC\nZiPKfBW9WWO9/NwoXT4OkgIgTxPlFnsSc5OsSZy/C+cW3QL4kAZL/uXa+RkgDVjPQ3wPCxLHdgUq\nxxjGfAB4wdzvT7TRRq1ztTsRFJ8t18ZH9fS9Slv3mNlgYg6z6Yn51YolMcvk5535LFeCrC3z0Unl\n09x9VCqNeAJRdm8IcLaZ3Ue6jwJPd1KgpL33oHrMQeWe3da58UYX9qNeXXaPGcffqYhIl1EGkIiI\niIiItKpX3f2p3L8H3P00olSYEwM6A4lJ4+s1VimcEmOVYGuLu18B7EQlQ6g7MVH10cQA5Udmdn4q\ntZQ3c6NtZds3s3wmxD7Escj6Pi2wIVHa5iUze9XM/p7mOmpEPounnoHhD3O/z1j4LCt5NCu5DB0z\n+y2RUQFjZzO09/jMUOX976uUbavHbLnfy+YXyvuwjc+7Smcfr//VWL694xHV2vqyyvtQySqBsYOv\nXcrdR7r7Te7+F3dfBFgGuDu3SD8z+0Wj2zWzqcxsfzN7AviaKFF1GzCIKOG1KJWsuaJGzsVaJgV+\nQSWwnQVtOuJAKgG+PmlupbZ09nnbLu5+InAoUaINovTe74kAwpPAO2Z2upn16mBT1a6rztBZ58a4\n0qX3mHH4nYqIdBkFgEREREREZKKS5qDZgEowZzczO7jO1RsO7tTL3S8gBlO3Ai5jzMG3GYk5fJ40\ns91z7+ezGS4gnoSv99/ocnHu/oO770eU89mLeLL5m9y2f0UMgr1sZss1sFuNDrbny2EVB6+vozLY\nly8Dl03S/S1wRWGd/PH5G/Ufm9Wq9K8jT3o3EoSoVdarK2XHKyv9Ve+/e6tsr9rxyn8vtzbYVrPm\nRyplZpOb2TxmtqyZTdXW8u7+OLA2cFd6qwewaoNtzktkGJ1EHJPuwPfAc8CVxNxmKwBnV9lEZ2ZB\nfU5kOGX7U2/QplSab2a33FuDCuUgy2T78wmNnUuXjbWlDnL344hMyp2A6xnzoYE5iWyvF81sgw40\n0xkZJ9XGA8eXSkH1jld2+T1mHH2nIiJdZny5sYuIiIiIiIwz7v6Kmf2ZSsbIX83sDnd/rNZ646Bf\nXxODkpel+X0WJ5423hT4LREgOcPMbk3ldz7Lrf5TG+Wa6mn/A+D/gP8zs8mJievXBrYkys1NDww1\ns4XK5jkqke/frHUsn3/6PL8u7v6NmV1JDMJtbGa7EfOrZIPN16e5HKq1/01Hj08H5UsDtpVJVcx+\nGlc+I8ojTtnFxyr/vUza5O+lo44CDkm//4EYbK7J3X80szOozI81V4NtXgrMn/t9EPBYcT4gM+tX\nZf388Z8JKJ2Pqg4/A2u5+xOprWeIDIlBZnanu7crg8TdbzazocA2xH3jdCJDsZpsf6YBnqnz3tRl\n3P1zIiB/QSpFtixxH98CWIQ4RheZWS937+xsnnoDzWPNM5cUz43O1tH+1atT7zFN/k5FRDpEASAR\nEREREZkoufslZrY5kQ3UA7jQzJZqY1L1LmFmswELAU9lQYw0r8BT6d9JZnYqsB/x/7jeROm614lM\nnqmA5eto5yBgBPCau9+R3puEmD9kXnfPnuInlTq7G7jbzAYAw1MbCwAGvFjHrr1OlOaahghgtSW/\nD2XzDl1EBIB6EoPnP1IJGpVNZv9clW2PxcwmIwby3yEmDX+wjv424gNiUHJG2p58folObrtezxEB\noPnNbGZ3/6Tagma2JXHs3wTucPev6m3E3b8zs1eJIMbSZtbd3atm16XgwqSprdtqLdsEr+R+rysA\nlORL1r1bb2NmtgyVa+lud9+6xuK/rPL+C7nfFydKWVVr7z6iJNb97l4MKP3g7k/A6KD634FjiKDN\nQGDbGn1ry75E8HlWIsvvlRrLPkdc31MQ107VuWvMrDcxYP8mcE+tc7xRqZTfQsDw7BxNPx8EHjSz\nY4gMrU2JY7oScEtntZ/kr41aGWnVzo03iOzPKYlzo6r0EMUBaZ1d3b3Wd9RZ/atLZ91jxpPvVESk\nQ1QCTkREREREJmb9qJQV+w3wl3HdATPbhggODCcGkaq5Off7FBCl26jMJ7Koma1co501gOOJslCH\n5j46hwjU3Glm85Wtm4JBdxbbb4u7/0SlNNTSZrZ0jf7ND6yVXj7t7mVzBt2f+gqwEbBZ+v1DYv6T\nYvvvAs+nl71T6axqtgEGAOcBu9RYrl3SsbguvVyuZD4nAFLm1zad3X6dsmPYjbg2SplZT+K8GQj8\nk/aVpMrampHIMKvW1vzAmURm2qAuCP50NFvkJiol+3YyswXqXG+99PMn4ryut0/z535/vNpCaU6S\nlXJv5R8AvjvXRtUAkpnNmbaxCDB1jT5lTqQSXNrGzNatY51SKXtor9xbh9VYPH/t71FtITOblJij\n6GTgKsbep3afC2Z2OPA2MAz4XdkyKaifDxDm76OdlbU0Ivd7rxrLlX436W9KVtJxZTObu8Y2NiAe\nHlgeeC/3fq37QYf616AO3WM64TsVERkvKAAkIiIiIiITLXd/Dzgi99aRTZjMeRgxfwfA4TXmu8gP\n1D6a+/3U3O9DygbszGxWYsA+c0bu93/nt5UCEMX1pwL6pJdfUJ6dU02+f5ekbKfi9mcgSt9lcwCd\nXLahNNh2SXq5YfoH8M8UYClzSvrZgyitN31J+wsSg9cQg5eDqmyrowYRA/4AF5f1hchCWqaL2m/L\neVQCooenoOEYUsbYEGDabJ1UurBRp1M5FgPLAmLpvBtKZezijOIyneC73O/TNLpyClRm5cmmBoaZ\n2Sq11jGzbakEN/7l7v9toE/5jJW1Ujmq4vZnJ7ISJsu9PXmuz+8C16SXvzeznUq2MQkwmEqZrvPK\n96bC3b8n5u/JAgBnm9m0NVZpa3tXUAma9qix6HVUAsM7mtmfqix3GpAFga+vcdwbPg8Y8z56nJmN\nFQhIx/SP6eXPjJmp1KHzMJMy8bJjsZKZLVnSj42BTWpsJrv/TUqUPJu8uECa52md9PLSQgZg1ePY\nSf2rV0fvMR39TkVExgsqASciIiIiIhO7M4G+xOTPU6XX69VaoTO5+4dmNgjoTwxOPmNmA4lJ3r8g\nSuFsT5SXArjL3e/PrX+XmZ0F7A78Cng6rX9PWmQZIrNpzvT6WnfPBlUBbiACSssSWTWPpu29Qgz+\nLkQMVi+Slj+pkQF/d78n7d9ewK9z+3c/MWC2XOrfL9Iql7r70BqbvBg4Mrc/2XvVDCEyq9ZLbT1r\nZqcBDxNPa69MlJvKgjED3b1qSayOcPenzOxEIsizBPCUmR1PlOCamTgPNwO+or6Mi87u3wgz2wW4\nnAgY3GZmFxAD7COABYnvMcvkeoPImmpPW6+Y2aHACcRcIw+b2ZnE0/TfAosS50WWUfMIcW12tvzc\nTP3N7DNi4Pv+FHCsxyFEP/sQGRH3mtmdxITxDnxOnF+LEt/vCmm9Z4A92+jTEWZ2CjCJuz9EXDfv\nA3MQ59BdZvZ/wH+JTIdViTKJMxe2WZxTZV9gtbTcuWa2OhGE/STty95UApFD3f0e6uDu95tZlkU3\nNxFY3b2edavYI/WzLFiatfmjmW1HZDb1IALNGxDzI31AZJrsSmXOpc+J/S96n7jfLW5mOxPlNz93\n99fa6mS6tq8m7jXLEfe504lSmd8D8xFZddl3P6QQgPqEyCTrAWxlZsOIa+4Vdx9jPrQ6XExcl92I\na/hY4vqZPvWvb9rXnpSUYXP3m8zsUmArIivz8XTPfpaYv2x9YOe0+MeMnZ31PvG3bB0z24w4N99L\ngccO969eHb3HdMJ3KiIyXlAASEREREREJmru/lOq//8g8RTwuma2mbtfNQ67cSgxcLwZEdg4scpy\n91Mpe5a3FzGgtS8xD8HRVda/hkJ5MXf/2cw2IcrlLEwM7pc97T+KmHfob7V2pIp9iafC+xNzehxb\nZfunAQfX2pC7v25m9wNZlsUz7v50jeVHpbmezicGNH9BJSuo6Exg/1rtd4LDiP+LH0B852cVPv80\n9eHCLu5HKXe/wsx6EBljUxED57uWLPo8sIG7jyj5rN62TjSzUcT5MCWx32XH/z5g4y6an+tRYt6n\nXxCBhgfS+/MRAa42pXvIH4lA0EHEvqyZ/lXzL2CfKhPGD6Myd9YW6d8PZjaNu3+bMohuTO2sQuVa\nyHuZOOdPSK8XAf6T6/O7ZrZa2s68xDw7ZZkzV1IZ7K/XQUR23mzAbmZ2qbvf28Y6pdz9fTPrT+xL\nreUeSCXnLiMG+/9IJTMj7x2gj7u/WfLZNcDqxPV5bnrvYiIAX4+diPvbKkRQ4f+qLHcNhVJ16Ry6\ngQg2zEmlrNiONH4vOIk4l1cjAjanFT5/jwiI30H1AMsORPbMNsS5c27JMm8BG7n7+4X3ryHu9dMQ\n5w/AX4GjOrF/demEe0y7v1MRkfGFSsCJiIiIiMhEz90fYcwSaaeb2XTjsP3v3X1zYk6FK4mB52+J\noMnbwNXEIPDv3P3zkvV/cve/EFlMZwMvEQPIPxATzF8NrOfum7r7NyXrvwMsRTypP4x4av77tI2X\niWOzgrv/uYGsiPz2f3b3A1L/Bqdtfk1kOD1PDKot4e796xzkvyj3e63sn6z9b9x9ayI74iLgtdT+\nd8Sk35cAK7v7nu7eWXNxVOvLKHc/kJg34zLiu86+58FEVsdzXdmHtrj7P4kAyDFEgORzYvL2T4k5\nnfoBS7l7XQGSNto6CTCiVOAzwMjU1ofEvFdbA6ulOWE6Xboe1iIy4T4jzvt3iOyVRrbzvbsfTWTh\n7UEl++dT4jr8iMj0OhlY1t3/5O6fVNnW+6lPdxLH4ztiUPyX6fM7iWvpPOL8+T79e4+4fnclzqOz\niPMc4v5RbOd5Iui7N5Ex+Clx7D8iAkMbuvsWaQ6wRo5FPsOmG3CemU3ZyDYK27uA2K+2lruDOG8P\nIgb0PyH2ZwQR2DsQWNjdq82dlAWAXyLuvyNpoBybu48kghrbEMfvHeJ7+Yb4noYC66T7cNkx3ZGY\no+hdKudMMZOrnn58TZw/OxPz+YxIfXiRCIQs6u5PtbGN79x9WyIg9i8i2PMd8TfhMeKhhWrbOTS1\nk52bn5HLQOuM/jWiI/eYTvhORUSartuoUe2Zq1FERERERERERERERETGV8oAEhERERERERERERER\naTEKAImpZP8fAAAgAElEQVSIiIiIiIiIiIiIiLQYBYBERERERERERERERERajAJAIiIiIiIiIiIi\nIiIiLUYBIBERERERERERERERkRbTvdkdEBERkdb1448/jfr886+b3Q2Ric4MM0yFrj2R5tD1J9Ic\nuvZEmkPXnkhzzDLLtN3qWU4ZQCIiItJluneftNldEJko6doTaR5dfyLNoWtPpDl07YmM3xQAEhER\nERERERERERERaTEKAImIiIiIiIiIiIiIiLQYBYBERERERERERERERERajAJAIiIiIiIiIiIiIiIi\nLUYBIBERERERERERERERkRajAJCIiIiIiIiIiIiIiEiLUQBIRERERERERERERESkxSgAJCIiIiIi\nIiIiIiIi0mIUABIREREREREREREREWkxCgCJiIiIiIiIiIiIiIi0GAWAREREREREREREREREWowC\nQCIiIiIiIiIiIiIiIi1GASAREREREREREREREZEWowCQiIiIiIiIiIiIiIhIi1EASERERERERERE\nREREpMUoACQiIiIiIiIiIiIiItJiFAASERERERERERERERFpMQoAiYiIiIiIiIiIiIiItBgFgERE\nRERERERERERERFqMAkAiIiIiIiIiIiIiIiItRgEgERERERERERERERGRFqMAkIiIiIiIiIiIiIiI\nSItRAEhERERERERERERERKTFdG92B0RERKR1bdD/+qa2f8HBazS1fRERERERERGRZlEGkIiIiIiI\niIiIiIiISItRAEhERERERERERERERKTFKAAkIiIiIiIiIiIiIiLSYhQAEhERERERERERERERaTEK\nAImIiIiIiIiIiIiIiLQYBYBERERERERERERERERajAJAIiIiIiIiIiIiIiIiLUYBIBERERERERER\nERERkRajAJCIiIiIiIiIiIiIiEiLUQBIRERERERERERERESkxSgAJCIiIiIiIiIiIiIi0mIUABIR\nEREREREREREREWkxCgCJiIiIiIiIiIiIiIi0GAWAREREREREREREREREWkz39qxkZr2AN3Jv3ePu\nq5nZasDdVVb7HhgBPAEMcvebG2xzRmAbYHNgPmBm4DPgUeAS4Cp3H1VYZziwamFTo4DPgaeBM939\n6sI6+X24093XqtGnTYBs/R3cfUgb+zCq1uc5O7j7kCr9r+Yid+9bpd0pgG/SyxHArO7+Q5Vl5wLe\nBroBg929X3q/H3AWcIi7H1+rI2bWG7il5KOfga+AV4ArgYHu/m0b29oXOA3Yz90Hlnw+K3APsBBw\nPrBL8TyYEJjZdcBGJR/9QOU8P8Xdh1dZvxewK7A+MA/QA3gVuBY41d1H1mi7J/ABMAWwm7ufU7LM\nQGCf3Fujz3czGwH0TO+PdPfpzWx64jor8xMwEngRuBg4N/vOzOwpYPHcsvO6+5s1+p6dH2W+BT4B\nHgSOdfenqm2ns5hZD+JcPbGL28n2u+Z9x8z6EOfA0e4+oCv7VKMP2fmxibtfW2WZAcBRwMbufl0n\nt1/t2gL4EngPuB04xt0/6kA7MwObuvvg9m6jynaza+ked1+tHevXda6IiIiIiIiIiEjraVcAKMeB\ny4A3C+8/DRQH8aYhBnZ7A73NbEt3v7yeRsxs1dTO7KnNG4lB8TmBdYENgFvNbBN3/6ZkE6cTgQ+A\nyYjg0brAVWa2r7ufXqXpVc1sRnf/rMrnm9XT/4KRwFiBjIJsoHoIMLzw2VFVtlHv4Pb0wOrEgGeZ\nTYngT2d4HPh37vUkqf31geOANcxsnfYGbMxsBmAYEfy5kAk0+FMwmAjGZCYD5iW+l3XNbCt3vyK/\ngpltC5wNTAncCdxFXNurAEcC25rZ79z9nSptbkEEf74GdgbGCgABtxLX0PLAOiWfZ+dkMaD3Yepb\n3uTAAsSg/Epp/w5Jn51NXOdbAlalv2VuAx4qvDcTsCIRNF7fzFYcB0GgfwNrA10aAGrAS8DRjH0f\naYZBZnanu/+vSe0Xr61uxN+QdYA9gbXNbNn29M/Mpib+Nj2b2ulM3xLf4ZvtXP+htH6XB0BFRERE\nRERERGT80tEA0EtVnip/qtrT5ma2I5GpcZKZXeXuP9VqwMwWJgZ3fwK2dvdLC59PTQy4/Qn4G9C/\nZDMDi1kE6anqZ4G/mdmFJYN+HxAD0RsSgZhivyYnAhlfEsGteo2o90n8sqe1zeyoRrZR8CEwG7AJ\n1QNAm9H4PlXzWFk/zewQIqPl90Rg46pGN2xm0xLnxWJEFsnOLRD8ATi7LEhhZusCNwFnmNnV2XVj\nZusT+/8OsEF+XTPrRgRW/g7cZmaLVbnetiOyIP4N7Gpmi7r7s/kF3P1WIsi6L+UBoGrn5Ac17gW/\nBe4D9jezQe7+nrufnT5bgsYCQLdWyRDrRgSm9gaOJQK/XWm2Lt5+Q9z9JWBAs/uRzAUcD+zRpPar\nXVtTA3cQwc1+tC941wOYsWPdK5eyJAd0YP2HGDs4KiIiIiIiIiIiE4FxPgeQu18A/BeYG1iwjlXO\nJzIGdi4Gf9L2vgJ2JErS7Z4CA/X0YwRRGmkaYOmSRW4hytZtXGUT6wDTEtlIE4o3iafAN0oD42Mw\ns9mJjIwu3af0nf0jvWx4QN7MpiKCIcsC/yRKG/3ceT0c/6SSic8TAYalYXRpv/OI0nrrFge33X2U\nux9LnMsLE5kwYzCzeYnv/A7gmvT2Ll20G2Nw94dT37pTf6nDRtsYRWXwfPWy817GiY+Bd4F+ZrZS\nszuTl+5HWdBnzWb2RUREREREREREpDN1NAOovT4m5imZotZCZrY48VT202XBn4y7f29mfycCSlMA\nX9TZjx/Tz+9KPvsfUV5sbTObOg0S5m0GvAU8AmxVZ3vjg6uBY4jSWA8UPtuEKIt0NV2/T++mnzM1\nslLKvLqWKG92GbB9teCPma0HHEAETLoR808dn59/KmWaPAkcSJSS24rIgNrO3W81szmBg4A/EEHL\nn4m5dYYQmWWjctvaCPgL8BviPHyZmJ/q9LYy3er0LrAIlWO2PhEQuraYsVNwJBEwe6Tks+2IY3Mb\nUT7uU+BPZnaAu5ddF50tm3Ol5r2gg0YQwdzu6d/o+a/qOUfScosQGYbLALMQ38UNxLwxnxXnPErz\nfV3v7n3S6wWJ82hNYI7UnxeA/3P3SwptTQbsT2Q1zkvcL4cBR7n7u1SR5sO6D/gVkS15RdkcQGme\nJYjr/SRgDaLU4IPAYSkwl9/uwmnfVyG+pzuITMtHgPuzfWzD18QcUtcB55jZku7+fVsrmdk8RNnL\n3kTpzveA64nj/kkd7dar9Dw0s0mJ7LEdiLKFXxFzxB3p7i+mZbJjDFE2dBRpzrI0/9Fw4toaQGQK\nneLuR6cA7t5ECcYFiQcd3iPOq6PSQwqlcwDl5uSam/gutiDuC562f3FuH8aaAyjXr78SWVkrEPe2\nu4GD3N0Lx2HF1P/l0nI3ptevE/e3fds+xCIiIiIiIiIiMq6N8wwgM5uDKNv1HTE/RS3ZwOINbW3X\n3c9394Pc/eM6+9GTGAB9m/KBcYhAyBQUslTSAO0GtKN02Xjg6vRzk5LPNiMGgasOMnei+dPPutsy\ns+7AFcQcK1cA21QLrJjZX4iSZvMDQ4lMmXmBm8zszyWr9CdK0p0JPAY8YmazEaXqdiMCAwNTu/MC\npwKH5dr7A5FB88vU3lnEoPopdN58ML9KP7Nj9of087ZaK7n7Y+5+pru/XvLxtsA3wI3u/iNxTs9I\nlObrUmbWgwg+QMwb1lWyAMeL7p4P/tR1jpjZ3MS8SmsSpRMHEgPf+xKl9bpRmaflw7Ta0USAMgse\nPUpkYA0nzp1/A0sAF5vZNrm2eqS2/k4EqgYT96cdgHvNbOayHUz3s1vTvvQtzhNVYmbgP8B8wAVE\nJtaawF1m1iu33cXSchsQQYzBxFxuDwBTtdHGGNz9euL+szCVOZ+qMrNFieDsDkTm4iAie3Rv4NH0\nt6Sz9E4/R5+HZjZJ6u+pxMMCZxF/i/5A3B9+mxZ9CTgh/f5f4rvPl1xbgQiyXJbWfyidMzem9UYQ\nc1+dl5bfm/r/tlwH/DH9HELcIy4ys7Gy/UoYcC8xd9hZwMPEvFzD85m0ZvZ7IjC0IhF8u4TIgL2j\nzj6KiIiIiIiIiEiTjLMMIDObBliKGEybjHiC+5s2VsuCBM91sPl90xPPAJMST0pvmPqxYRr4LnM9\nMfC3MXBl7v21gOnTe8s32JfpzWxAjc8/yOZB6Qru/qKZvUjs0+j5ksxsFuB3ROZBl0qD2Pukl9fU\nWjZnEmLgccP0+u4awZ+FiKDLY8Ca2fxOZnYEkSFxmpndVJgXaibA8kESMzuemCR+c3e/Kvf+CUT2\nxtZEZgTEsRwFLJtlJqT2niHKXh1ST8ZDNWbWlxjcfYWYuwrgF+nny+3c5kppm1e6e5Y19y8i4LVz\n+r3TmdmURMbDACIAcaO7P9HJbXQDZiDO6UHp7QG5zxs5R7YHZgU2cfdrc9sYSmTprOjuDwADUjbI\nbIV5j44ApgOWc/dHc+uvTQTvtiYCUAB7ESX5LgB2zc31tDtRNnFvIqMrv69TEsGEJYhSmUNp21zE\n9dQ3y6AzsxOJbKgtiYAFREB0OmAddx+WljsKuJ+YI61RexH3z0PN7Iosi6aKC4n77BbuPvr+m+YQ\nOxb4PzoQqEzZPbMTge+DiOyek3OL7EQERM4G/pw7TicTQbmLzOzX7v5SulccBLxZMufVrERG4ehM\nLzPrTRyHM919z9z7UxD3ljXNbDZ3/5DapgR+7e4j0/o3ENl+OzHm36wyCwHHuns+kH0FEahcD7gs\nBSQHE3PwrZBlGprZscDjbWxfRERERERERESarKsCQNub2fZVPvuGeOr56Dq2M2v6+XnxAzNbgxjc\nLRru7sML7+1TshxEuZy5qjWeSjsNB9Yzs8lyA/ibEZlDD9N4AKgnUdKomqeJAceudDVwuJktkZs3\nZmMiyHIVlcBCRy1TCHZNShzvDYgMhIvdvd6nyA8kyp3dQcwXc7KZ3enur5Qs2ze1dUg2sA/g7l+Y\n2d+IgdE/EVkWmSdLMmSuAV6jkjWVbeclM/uAyvkJcewmJUqJ3ZaW+8bMVgP+10Dwp1/admZKYEli\nsPg7YLdc2bnp0896Sx4WbZt+5ssr3kec26uZ2a/c/bV2bjuzeCqJVebn1PbuHWwDImBzWpXPPiWO\nWz7Y2Jf6z5EsU3IZM7sud/z3Bv7i7h9R2znAv/PBn2R4+pk/j7YiysP1LwQ4zwN6Mfage/fU11WA\n3dMca/U6qVA+8WYiANQLwMzmA1YmAnTDsoXc/SszO4wINDTE3d83swOJoMI5Zva7fBnFTMr+WTq1\nXQxknEAE5TY2sxnd/bM6m3/SzKp99iIRcHsr995ORBZW//xxcvcXzGwI8GfKS2kW/UylRNzozRBz\n190+xpvu35rZQ0Qm2ixUMsqqOTsL/iS3EcGaXm2sBxGwLmYn3kwEgLL1V0l9GZQvM+nuH6Yg0Jl1\ntNN0s8xS19SAIi1J579I8+j6E2kOXXsizaFrT2T81VUBoKeJkjQQA9gbEeVmhgFbNjBgly03Q8ln\na5ArwVUwvPB63izbI5X16Qn8lijldJWZjZ4bocTVxOD7WsDNqQzZRsBF7j6qxoBiNf91916NrtTJ\nrgYOJ8rAZQGgzYCH3P0dM+usANDS6V/mJ6Lc0bPAP4HzG9jWbERG1ubAwcTcFf80sxVLMriyNtcz\ns5ULn2UltJYovP9GsUF3f4Qo9dTTYq6gBYjMleVSf/6XW3wwsDpwa8qwuoUYTL2nRoZZmd0Kr78F\n3icyRE5x93yptE/Tz7LroyaLuZS2AEamfgKQzulLiYDbTsChjW674EMqAc0eRPBuJWLAvY+7tyt7\nqcRtVMpuzUCUxZqNKG21b0kArpFz5F9EZtyhwI5mdivx/d5Sz73M3e+C0Vl2ixOZjUYEDyACUZnF\ngJey+V9y2/iByDApOprIUvuOxktyFY99FkiYPP1cNv0sK5HZVtCjlnOBbYjgwm6UB7yzY39v8QN3\n/9nMHiSO4aLAPXW2Oxj4gJjrqRdx/ncjsnvGuBelDLKliKygA0vu871y/WzrWHzi7l8W9uEN4A0z\nm8zMliXuK/NTCfbCmOdFNWN8h+7+k5l9ReU7rOXDQvAIxt05ME59/HF7Y+QiE7ZZZplW579Ik+j6\nE2kOXXsizaFrT6Q56g28dlUA6Kl8GZz0tPhQYlD2AjPbrM5B8WxQfv7iB+5+OBHEyNrIT8RdVXqS\n+3NioH4zIhhxLDF/QplriaecNyYGytcg5kiZEOf/AcDdnzKz14l9OtLMZiSCF2UDzB0x2N37ddK2\nbidKQf1gZscRQbhliWyqIwrLZpkxtSYmn7HweqxyhKls4UnEHCTZgOhbRIBxMXJzaLn75WY2EvgL\nsFr6+Rfgo1T+rd7MjCVzWVltyTKW5ieCq6VSqatfFYItG1AJHH1bJZC5vZkdUa3UXp0+KJbEMrPD\ngWOI4Ouq7j5Whl873OruA3NtHEUERHYnAsmHF5av+xxx91fNbDki4LwhkT3UF/jGzAYDB9S6n6W5\npE4ngqyTEpkXrxJz6ixHBCEws6mJspT/K99SqTmJwOhGRLDr9/Wu6O7fFd7KMnG6pZ9ZIOyDwnK4\n+0gz+7qBfubXHWVmuxLB5+NT2bKi6dLPYoAi8176OVUq57dlyTLHu/u3uddn56+tVMrtXuAsM3vf\n3W/OLTsVEbCcntoZm8X7SJnSUqdmth8xF9Is6a1PiTnYXiECS93K1isofocQ32NH1oU6zgEq34GI\niIiIiIiIiIynJml7kY5Lg6M7Ek/9b0QM/tbj+vRzky7q13PAx8Ac6en8smU+JJ503igNpG8KvEsM\n1E3IrgZ+Y2YLEN9Jd8bvoNYtWRZHOp/6EqWyDjGzFQrLZk/bz+Tu3ar8W7OONgcD/YDLiXKDM7j7\nPO6+PSWDp+5+q7uvTWWOqXOAaYHzzWzF4vKd4Nb0c+02llsDcDO7MffedunnlcR+Fv+9SQQX1uus\nzmbc/W9EhuCiRBZXPYPVjbYxgrhWRwKHmdkfC4s0dI64+0vuvi3x3a5MlIb7nAggHdBGd64hsk1O\nJzIPp3X3BRk7+PQNUS6sNHyfAkRFg9y9D1GObS0z265kmfbKAlHTFT9Ic8NM0d4Nu/tLxDHsSczl\nU5Q9OlStRGcWvPyUmMvmqJJ/NfuXSprtQAR6LjOz/IMG2XfxbI3zo5u71/u3bAxmtgsxH97rwPrA\nXO4+s7tvALzUnm12karnQJX3RERERERERERkPDJOAkAA7v41Mej8E1FSp825c9z9YeAJYGkz26aN\nxRvelzSIOTUx0PdljUWvJp7SXgXoA1xVNm/FBCab12ZjYqD84cL8F+O1FLwbQGRUDE3ZOpln0s9l\niuuZ2eJmdqKZ1cyUSKX+tgBedfft3f2+rCyXmc1EzNuSZW50M7MDzezQ1Lcv3P1Gd9+NSlZVscxY\nZ7iDCEZumOZMqSabA2tY6u8sQG+iPNtW7t6v+A84Ja2zcxf0G2BX4BPgD8AuXdGAu/8X2C+9PCtl\n4mTqPkfMbAsz+0eaB+xHd38gZSD2Tqusklt9VGFb8xCl3u509/7u/oi7f5U+/nX62S3192fgeWAh\nM5u2sJ1uwOtmVizF9UT6uTdRLvCUdH52hmy+oeVKPluWjv/9OIHY342JgGlelqlT7br5HTE/z8vu\nfl2V4MyIKuuO5u7XARcTQbchWTAyfRfPAQuYWc/iema2mZn91Sqpc43+Pdg6/dzU3W9y93w2zRjn\nRZPVOgd+Oy47IiIiIiIiIiIijRtnASAAd3+MeAp+EmIC8HpK0G0DfA2ca2a7pTl8RkuD7xun7UIE\nc+q1N1HqZ5i7l5bpSa4hBviOIwb+i5OST4geAd4hJp1fiwlzn04EHgXmA87IvX8xaYLz/GC4mU1B\nlPM7gAj81fIzkeUzjZlNldvGZGkbkxCZA6RgYB/gqJJATK/087+N7Fg9Unmr/VNf/m1mi+c/N7Me\nZnYCkcXzMjH3CsR33gO4vEZ5t38R+7+umc3RBX3/mCiRB3BCITjTme1cSJRam4GY8yvTyDmyBFFK\nrm9h873Sz/x3+0PazmTpdVaCbOb8vSsFeLL+9MitP5QoN3hsITNqZ+LeUzrPj7u/TtyfZqYSvOsQ\nd3+BuE9sYmajg1wpE+nYTtj+90Tw72di7pv8Z88ATwJrmtlW+c/MbH9gEeDGeoI8ddiPyARdiQhM\nZoYQWUQD83+rzGxeotzeQcScZpC+d6KEXz2y82KM897M9ibmiYIxz4tmGUb8ndjVzLLAFGY2K2OX\nVRQRERERERERkfFMV80BVMuRxFwYixKDrMfB6Dl8lgCuy8/T4O4vmtmqRBmus4HDzewOYk6CWYg5\nL34J/EgEAcoGP/c1s/xA4eTEE+QrEiWi+tfqsLu/bWaPAssTGRf/qbW8mS1BBASeSk+Y501vZgNq\nrU9MAn9ZG8tUa3t+Imj2qrsPrbZcmofjGiIIBvWXf9vVzHpX+exf7n5O/b0dk5ltSZRzuiyViKop\nTXi+PTFQvIOZ3eju17r742Z2DHGuPZ9Kn31JzHvzKyK4cX3VDTN6ovlLiYH3x83sJuK8WQ+Yhyg9\nNZOZTZmCh4cR8xT9x8yuBD4iBnJ7E0/RX5Pbz4OJgeXiHCUNc/fLzGwuYq6iJ9O18Qwxd8lqaX/f\nBNbPBTmzMmG1zo/P0twsmxNlsjo84F/SxiXp+1uTuHaLZdo6y+7EMdnSzC5291saPEfOII7ZWWa2\nHlHKck7i2HwOnJxr610iO+ZfZna7u5+TvpO1gIfM7G7iu9mQCEr9jygrlxlI3Dv2BJYzs/uBedN7\nzxNl06o5gbj2t0/7eVejB6rE7sB9wJ3pfvERsC5Rug0io7Pd3P1BMzsL+HPJxzsAdxPHchuiNNrS\nwKrE/HB7dqTtXB8+M7P+RFDweDO73t0/AAYR13tfYBkzu5N4YGALYv93TyVCcfevzexzYFkzO4MI\nTlWdl4u49noDw8zsMuIhh5WAFYhjPCtjnhdN4THn2m7ADcDD6Rz4mjgfswBVh84BERERERERERHp\nOuM0AwgglT/KBvuOMLNfpd/7EPM2LFGyzmPAYsBOxODrGkT2QB9iwPWvwHzuvk+uvFLePow5N8S+\nxJPyg4Gl3P35OrqelUy7po7yb0ukdvqUfNaT8vkq8v/KJjSv1/xpG22VzIPKPj2aymXVY15iALbs\n33yNdXUsWxJ9X6jeFdz9RWIQHyJLbI70/lFEoNHTdncmBtv/DGxfZwm/fYDjicDPnkRw4HkisDIo\nLbNuau9u4ry8jxjY3Q9YgBiUX8Pdf8ht92DqmKOkXu5+CjEwfiHwC2A3osTUl8SxWczdXwFIT/Ev\nTZTOerSNTV+Yfu7YFfP0JLsT2RBbmNm6XdFA2vcscPKPbC6des+RFAxYBbiICFzvS8y7dDWwnLu/\nlmvuCKJ82QZUyudtSQSvZycCrmsC9xJlta4BZjWzpVNb3xPBomOAGYG9iMDAecDqVe5v2X5+RyUo\ncnbKZuoQd3+C2Pc7iXN9h7R/66dFvu5oG8AhRJZJse2niRJ9FxHn7J7E+X0SsLS7v98JbWdtXULs\n4/Ska9tjrrF1qZRx3I0oV/c40Nvdzy5sZnfgvbRctSB51t4/ib9n7xIBpm2JTKgdiQATqe2mc/eb\ngXWAp4mg55bALVQy4jrjHBARERERERERkS7QbdSoxqeyMbNexBPY16cJyDuFmV0LXOHul3bWNpvF\nzPYBFk7zwIzrtv8IbO3uG43rtjsqZUic6O63NLsvXcXMJiWCHtO1UXpwvGNm+wKnATu4+5D03ghg\nhLv36sR2rgM2AuZ19zc7a7tSvzRH2tzAWykYkv9sSWL+oePc/dBm9E+6XgqWzgi8m+ZFyn+2MRHA\n3K2tzM8N+l/f1DnzLjh4jWY2L9I0s8wyLR9//EWzuyEyUdL1J9IcuvZEmkPXnkhzzDLLtHU9sD/O\nM4CqSZkbqxJPGU/Q0nwRm9GEfUmZGn9sRtsdlebWWJaYfL2VbQ68MaEFf2Si053IjnooBS2B0feY\n/dPLu5vRMRln5gDeopItCoye42ofYh6te5rQLxERERERERERqUNH5wBaKM1n82aWDdAB2wJ/TROP\nT+hWIjKkzm1C278mAnsnNqHtjtoR2Nfd3252R7rYFlTm4ZkgpHmflk//ymRzW33r7sd3oJ1+RKm0\nussAStdw92/MbAhRyi6bX2oUUQJxKSJbs9Y8NzKBc/dX0/fex8weAB4gSmL+gShxeaK7ezP7KCIi\nIiIiIiIi1XU0AGTEXCb3AEM6siF3nxADFqXc/R6a9FR0CqB1Wlm+ccndj2h2H8YFd9+k2X1oh97E\nE//VZHNbjSTmTWqvfsDiHVhfOtfuwJNEcHZHYFLgFWIepEE11pPW0YeYu2pr4nz4iZgL7Zg0d5KI\niIiIiIiIiIyn2jUHkIiIiEg9NAeQSHOoFrtI8+j6E2kOXXsizaFrT6Q5Jrg5gERERERERERERERE\nRKRzKAAkIiIiIiIiIiIiIiLSYhQAEhERERERERERERERaTEKAImIiIiIiIiIiIiIiLQYBYBERERE\nRERERERERERajAJAIiIiIiIiIiIiIiIiLUYBIBERERERERERERERkRajAJCIiIiIiIiIiIiIiEiL\nUQBIRERERERERERERESkxSgAJCIiIiIiIiIiIiIi0mK6N7sDIiIi0rpuPGUjPv74i2Z3Q0RERERE\nRERkoqMMIBERERERERERERERkRajAJCIiIiIiIiIiIiIiEiLUQBIRERERERERERERESkxSgAJCIi\nIiIiIiIiIiIi0mIUABIREREREREREREREWkxCgCJiIiIiIiIiIiIiIi0GAWARERERERERERERERE\nWowCQCIiIiIiIiIiIiIiIi1GASAREREREREREREREZEWowCQiIiIiIiIiIiIiIhIi+ne7A6IiIhI\n604lHKsAACAASURBVNqg//XjvM0LDl5jnLcpIiIiIiIiIjK+UQaQiIiIiIiIiIiIiIhIi1EASERE\nREREREREREREpMUoACQiIiIiIiIiIiIiItJiFAASERERERERERERERFpMQoAiYiIiIiIiIiIiIiI\ntBgFgERERERERERERERERFqMAkAiIiIiIiIiIiIiIiItRgEgERERERERERERERGRFqMAkIiIiIiI\niIiIiIiISItRAEhERERERERERERERKTFKAAkIiIiIiIiIiIiIiLSYhQAEhERERERERERERERaTEK\nAImIiIiIiIiIiIiIiLQYBYBERERERERERERERERaTPdmd0DGf2bWC3gj99Y97r6ama0G3F1lte+B\nEcATwCB3v7nBNmcEtgE2B+YDZgY+Ax4FLgGucvdRhXWGA6sWNjUK+Bx4GjjT3a8urJPfhzvdfa0a\nfdoEyNbfwd2HtLEPo2p9nrODuw+p0v9qLnL3vlXanQL4Jr0cAczq7j9UWXYu4G2gGzDY3ful9/sB\nZwGHuPvxtTpiZr2BW0o++hn4CngFuBIY6O7ftrGtfYHTgP3cfWDJ57MC9wALAecDuxTPgwmBmV0H\nbFTy0Q9UzvNT3H14lfV7AbsC6wPzAD2AV4FrgVPdfWSNtnsCHwBTALu5+zklywwE9sm9Nfp8N7MR\nQM/0/kh3n97MpieuszI/ASOBF4GLgXOz78zMngIWzy07r7u/WaPv2flR5lvgE+BB4Fh3f6radjqL\nmfUgztUTu7idbL9r3nfMrA9xDhzt7v/P3p1H2VWV+f9/hxmFZgyTbctkP2groIjNFwcwIGAYAjFg\nQDBAo8Cyv5IGRVAmBWWwaUF+toCCUVGmhEFEgkADX9qBSYITPKKCoN1IFAOozOT3x96XXG7urbqV\nVOVWXd6vtWqd3HvO2XufqnML1vnUs/cJIzkmSZIkSZIkjX4GQBqKBC4CHmh5/27gipb3VqI82N0J\n2Ckipmbmxd10EhHb1H7WqX1eRXkovh4wEdgVmB0RkzPzyTZNnEkJPgCWo4RHE4GZETE9M8/s0PU2\nEbF6Zj7aYf+Ubsbf4jFgoSCjReNB9QzgppZ9x3doo9uH26sC7wK+12H/eynhz3C4E/hO0+ulav+7\nACcDEyJix0UNbCJiNeA6SvjzVcZo+NPiHEoY07AcsAHl5zIxIvbOzEuaT4iI/YCzgRWBG4D/ovwu\nfwdwHLBfRLwzM3/Xoc+9KOHP34CDgIUCIGA25TO0FbBjm/2Ne7I10PtDHVuz5YHXUgKvt9XrO7ru\nO5vyOZ8KRIfxtnMt8KOW99YAtqaExrtExNZLIAT6DrADMKIB0BDcC3yKhX+PSJIkSZIk6WXIAEhD\ncW+Hvyqf0+mvzSPiQEqlxuciYmZmPj9QBxHxesrD3eeBfTLzwpb9r6Q8NH8/cBJwRJtmzmitIqgV\nCj8FToqIr2bm4y3nPEx5EL0bJYhpHdfylCDjL5Rwq1vzuv1L/HZ/2R8Rxw+ljRZ/ANYGJtM5AJrC\n0K+pkzvajTMijqZUtLybEmzMHGrDEbEy5b7YlFJFclAfhD8AZ7cLKSJiInA18IWImNX43ETELpTr\n/x2wa/O5ETGOEqx8Brg2Ijbt8Hn7APA/lPDiQxHxxsz8afMBmTmbErJOp30A1OmefHiA3wX/DNwC\nfDQizsrM/8nMs+u+zRlaADS7Q4XYOEow9RHgs5TgdyStPcLtD0lm3guc0OtxSJIkSZIkaXRwDSCN\nqMw8H/gt8GrgH7s45TxKxcBBreFPbe+vwIGUKekOrcFAN+OYR5kaaSVgizaHXEOZtm6PDk3sCKxM\nqUYaKx6gVApNqg/GXyIi1qFUZIzoNdWf2X/Wl0N+IB8Rr6CEIVsC36RMg/XC8I1w9KlTJv6cEjBs\nAS9O7fcVytR6E1uDo8ycn5mfpdzLr6dUwrxERGxA+ZlfD1xW3/7gCF3GS2TmrXVsy9D9VIdD7WM+\nCwKQd7W77yVJkiRJkqSXCyuAtCTMpaxTssJAB0XEZpQpp+5uF/40ZOYzEfEZSqC0AvBEl+N4rm6f\nbrPvccr0YjtExCtraNFsCvAgcBuwd5f9jQazgBMpU2N9v2XfZMr0b7MY+Wv6fd2uMZSTauXV5ZTp\nzS4CpnUKfyJiZ+BjlMBkHGX9qVOa15+qlSZ3AUdSppLbm1IB9YHMnB0R6wEfB95DCS1foKytM4NS\nWTa/qa1JwOHAGyj34S8p61OdOVilW5d+D/wTC75nu1ACoctbK3ZaHEcJzG5rs+8DlO/NtZTp4/4E\nvD8iPpaZ7T4Xw+2Ruh3wd8FimkcJc5epXy+uf9XNPVKP+ydKheFbgPGUn8W3gRMz89HWNY/qel9X\nZubu9fU/Uu6j7YB163h+Afx/mfmNlr6WAz5KqWrcgPL78jrg+Mz8PR3U9bBuATaiVEte0m4NoLrO\nEpTP++eACZSpBn8IfLIGc83tvr5e+zsoP6frKZWWtwH/3bhGSZIkSZIkjX5WAGlERcS6lGm7nqas\nTzGQxoPFbw/Wbmael5kfz8y5XY5jFcoD0Ido/2AcShCyAi1VKvUB7a4swtRlo8Csup3cZt8UykPg\njg+Zh9HGddt1XxGxDHAJZY2VS4B9OwUrEXE4ZUqzjYELKJUyGwBXR8SH25xyBGVKui8CdwC3RcTa\nlKnqDqYEA2fUfjcA/gP4ZFN/76FU0PxD7e9LlIfqpzN868FsVLeN79l76vbagU7KzDsy84uZ+Zs2\nu/cDngSuysznKPf06pSp+UZURCxLCR+grBs2UhoBxz2Z2Rz+dHWPRMSrKesqbUeZOvEM4DfAdMrU\neuMoax99ijLNIvXfF9Xz/4lyH+1JWYvnP2q/mwNfj4h9m/patvb1GUpQdQ7l99MBwP+LiDXbXWD9\nfTa7Xsv+retEtbEm8ANgQ+B8SiXWdsB/RcT6Te1uWo/blRIQnkNZy+37wCsG6UOSJEmSJEmjjBVA\nGhERsRLwZsrDz+Uofzn/5CCnNUKCny1m99MjYl7999KUCord6jh2qw++27mSUiW0B3Bp0/vbA6vW\n97Ya4lhWjYgTBtj/cGMdlJGQmfdExD2Ua3pxvaSIGA+8k1J5MKLqQ+zD6svLBjq2yVKUaprd6usb\nBwh/NqGELncA2zXWd4qIYykVEp+PiKtb1oVaA4jmkCQiTgHWA/bMzJlN759Kqd7Yh1IZAeV7OR/Y\nMjP/2NTfT4BDIuLozHymy2ttd037UwKg+yhrVwH8fd3+chHbfFtt89LMbFTNfYsSeB1U/z3sImJF\nSrXeCZQA4qrM/PEw9zEOWI1yT59V3z6haf9Q7pFpwFrA5My8vKmNCyhVOltn5veBE2rFzdot6x4d\nC/wd8NbMvL3p/B0o4d0+lAAK4P9SpuQ7H/hQ01pPh1KmTfwIpaKr+VpXpEzbuDllqswLGNyrKJ+n\n/RsVdBFxGqUaaipwSj3ui3XsO2bmdfW444H/pqyRJkmSJEmSpDHEAEjDYVpETOuw70ngVMpfyA9m\nrbr9c+uOiJhAebjb6qbMvKnlvcPaHAeQlAeh7XeWqZ1uAnaOiOWaHuBPoVQO3crQA6BVgOMH2H83\nMGIBUDULOCYiNm9aN2YPSsgykwXBwuJ6S0vYtTTl+70rpQLh65l5fZdtHUmZ7ux6ynox/x4RN2Tm\nfW2O3b/2dXTjwT5AZj4RESdRgrv3U6osGu5qUyFzGfBrFlRNNdq5NyIeZsH9CeV7tzRlKrFr63FP\nRsS2wONDCH8OqW03rAi8iRI6Pg0c3DTt3Kp12+2Uh632q9vm6RVvodzb20bERpn560Vsu2GzOh1a\nOy/Uvg9dzD6gBDaf77DvT5TvW3PYuD/d3yONyti3RMQVTd//jwCHZ+YjDOxc4DvN4U91U90230d7\nU6aHO6Il4PwKsD5wZ0sby9SxvgM4tK6x1q3PtUyf+F1KALQ+QERsCLydEtBd1zgoM/8aEZ+kTCso\nSZIkSZKkMcQASMPhbuCK+u8VgUlAUNaxmJqZj3bZTuO41drsm0DTFFwtbmp5vUGj2iMilqKEMP9M\nmcppZkQckJkzOrQ1i/LwfXvgu3UasknA1zJzfkR0dyUL/DYz1x/qScNsFnAMZRq4RgA0BfhRZv4u\nIoYrANqifjU8T1mP5afAN4HzhtDW2pSKrD2Bo4BPA9+MiK3bVHA1+tw5It7esq8xhdbmLe/f39ph\nZt5GmQpulbpW0GsplStvreN5vOnwc4B3AbNrhdU1lAfqNw9QYdbOwS2vnwL+l1IhcnpmNk+V9qe6\nbff5GFBdS2kv4LE6TgDqPX0hJXD7F+ATQ227xR9YEGguSwnv3gbcA+yemYtUvdTGtcCP6r9XA95H\n+Rl9CZjeJoAbyj3yLUpl3CeAAyNiNuXne003v8sy87/gxSq7zSiVjUFZhwtKENWwKXBvZs5raeNZ\nyhpCrT5FqVJ7mhKODkXr9/6xul2+bres23ZTZLauHzbqjR+/cq+HII0Kfhak3vHzJ/WGnz2pN/zs\nSaOXAZCGw5zmKZDqX4tfQHkoe35ETOnyoXjjofzGrTsy8xhKiNHoo7HY+YDqX7z/mfKgfgoljPgs\nMKPDKZdTpkHag/KgfAJljZSxuP4PAJk5JyJ+Q7mm4yJidUp40e4B8+I4JzMPGaa2vgfslZnPRsTJ\nlBBuS0o11bEtxzYqY6YP0N7qLa8Xmo6wTlv4Ocr6K42H4g9SAsZNaVozLTMvjojHgMOBbev2cOCR\nOv1bt5UZb2qqyhpMo2JpY0q42lZELA1s1BK27MqC4OipDkHmtIg4ttNUe116uGU6NCLiGOBESvi6\nTWYuVOG3CGZn5hlNfRxPCUQOpQTJx7Qc3/U9kpm/ioi3UgLn3SjVQ/sDT0bEOcDHBvp9VteSOpMS\nsi5NmSrwV5Q1dd4KjKvHvZIyLeXj7Vtqaz1KMDqJEna9u9sTM/PplrcalU3j6rYRhD3cchyZ+VhE\n/G0I4+y5uXMXtVBO6h/jx6/sZ0HqET9/Um/42ZN6w8+e1BvdBq9LDX6INDT14eiBlL/6n0R5+NuN\nK+t28giN62fAXGDd+tf57Y75A+Wv3SfVB+nvBX4P/HAkxrQEzQLeEBGvpfxMlmF0h1rXNKo46v20\nP2WqrKMj4v+0HPuXul0jM8d1+Nquiz7PAQ4BLqZMN7haZr4mM6dRKi5eIjNnZ+YOLFhj6lxgZeC8\niNi69fhhMLtudxjkuAlARsRVTe99oG4vpVxn69cDlHBh5+EabENmnkSpEHwjpYpr3CCnLEof8yif\n1ceAT0bE+1oOGdI9kpn3ZuZ+lJ/t2ylTw/2ZEiB9bJDhXEaptjqTUnm4cmb+IwuHT09SpsVr+1/r\nGhC1Oiszd6dMx7Z9RHygzTGLqhFE/V2bsSwLrDCMfUmSJEmSJGkJMADSiMjMv1EeOj8PHBkRg66d\nk5m3Aj8GtoiIfQc5fMj3bn2I+UrKQ9e/DHDoLGA8ZZ2N3YGZTeuAjFWNdW32oDwovzUzH+zheIak\nhncnUCoqLqjVOg0/qdu3tJ4XEZtFxGkRMWClRJ3qby/gV5k5LTNvaUzLFRFrUNZtaVRujIuIIyPi\nE3VsT2TmVZl5MAuqqlqnGRsO11PCyN0i4o0DHNdYA+u6Ot7xwE6U6dn2zsxDWr+A0+s5B43AuAE+\nBPwReA/wwZHoIDN/C/xbffmlWonT0PU9EhF7RcR/1nXAnsvM79cKxJ3qKe9oOn1+S1uvoUz1dkNm\nHpGZt2XmX+vu19XtuDreF4CfA5tExMot7YwDfhMRrdOx/bhuP0KZLvD0en8Oh8Z6Q29ts29L/P8F\nSZIkSZKkMccHOhoxmXkH5a/glwLOrQ/ZB7Mv8DfgyxFxcF3D50X14fsetV0oYU63PgK8ArguMxea\nAqzJZZQHuydTHvxfOoQ+RqvbgN9RFp3fnrF5TacBtwMbAl9oev/rlJ/Xac0PwyNiBcp0fh+jBH8D\neYFS5bNSRLyiqY3lahtLUda0oYaBuwPHtwli1q/b3w7lwrqRmU9R1qZZCvhORGzWvD8ilo2IUylV\nPL8Evlx37V3HfvEA07t9i3L9EyNi3REY+1zKFHkAp7aEM8PZz1cpU62tRlnzq2Eo98jmlKnk9m9p\nfv26bf7ZPlvbWa6+fqpu12z+3VUDnsZ4lm06/wLKdIOfbamMOojyu6ftOj+Z+RvK76c1WRDeLZbM\n/AXl98TkiHgx5KqVSJ8djj4kSZIkSZK0ZLkGkEbacZS1MN5Iech6Mry4hs/mwBXNa6Bk5j0RsQ1l\nGq6zgWMi4nrKuhTjKWte/APwHCUEaPfwc3pENC+qvjxlSq+tKVNEHTHQgDPzoYi4HdiKUnHxg4GO\nj4jNKYHAnMy8omX3qhFxwkDnUxaBv2iQYzr1vTElNPtVZl7Q6bjMnB8Rl1FCMOh++rcPRcROHfZ9\nKzPP7X60LxURU4FNgIsy897Bjs/M5yNiGnAXcEBEXJWZl2fmnRFxIuVe+3md+uwvlHVvNqKEG1d2\nbLi0/UJEXEh58H5nRFxNuW92Bl4D/AlYIyJWrOHhJynrFP0gIi4FHgE2o1SJ3EkJERvXeRRl+qxT\naoizyDLzooh4FWWtorvqZ+MnlDVutq3X+wCwS1PI2ZgmbKD749GI+DawJ2UNpGF/4J+Z36g/v+0o\nn93WadqGy6GU78nUiPh6Zl4zxHvkC5Tv2ZciYmfKVJbrUb43fwb+vamv31OqY74VEd/LzHPrz2R7\n4EcRcSPlZ7MbJZR6nDKtXMMZlN8d/wq8NSL+G9igvvdzytRznZxK+exPq9f5X0P9RrVxKHALcEP9\nffEIMBFYpe5fnPWhJEmSJEmStIRZAaQRVac/+nB9eWxEbFT/vTtwPCUEaj3nDmBT4F8oD18nUKoH\ndqc8cP00sGFmHtY0vVKzw2rbja/plL+UPwd4c2b+vIuhN6ZMu6yL6d82r/3s3mbfKi1jafc1tYvx\ndLJxbWOwKfNgwTXdXqfL6sYGwDYdvjYc2lAXMpUy9k26PSEz76E8xIdSJbZuff94StCYtd2DKA/b\nPwxM63IKv8OAUyjBz79SwoGfU4KVs+oxE2t/N1Luy1sooc+/Aa+lPJSfkJnPNrV7VL3OYVlDJTNP\nB7YAvgr8PXAwsA8l0DgO2DQz7wOIiNfVY3+ZmbcP0vRX6/bAkVinpzqUUiWzV0RMHIkO6rU3gpP/\nbKyl0+09kpkPU6Z5+xoluJ5OWXdpFvDWzPx1U3fHAnMo90pj+ryplPB6HUrguh3w/yhTq10GrBUR\nW9S+nqGERScCqwP/F3gb8BXgXR1+vzWu82nKfQpwdq1mWiyZ+WPKtd9AudcPqNe3Sz3kb4vbhyRJ\nkiRJkpaccfPnj/WlTTTSImJ94H7gyroA+XC1ezlwSWZeOFxt9kpEHAa8vq4Ds6T7fh+wT2ZOWtJ9\nL65aIXFaZl7T67GMlIhYmhJ6/N0gUw+OOhExHfg8cEBmzqjvzQPmZeb6w9jPFcAkYIPMfGC42lX3\n6hpprwYezMznWva9ibL+0MmZ+Ymhtr3rEVcu8f/ROP+oCUu6S2nUGT9+ZebOfaLXw5Belvz8Sb3h\nZ0/qDT97Um+MH79yV3/AbQWQeqJWbmwD3N3rsSyuurbRFHpwLbVS43296HtxRcQGlOmzftbrsYyw\nPYH7x1r4o5edZSjVUT+qoSXw4u+Yj9aXN/ZiYJIkSZIkSVo0rgGkodikrmfzQKMaYDHsB3y6Ljw+\n1r2NUiH15R70/TpKkHtaD/peXAcC0zPzoV4PZITtxYJ1eMaEuu7TVvWrncbaVk9l5imL0c8hlKnS\nup4GUCMjM5+MiBmUqewa60vNp0yB+GZKteZ1vRuhJEmSJEmShsoASEMRlLVMbgZmLE5DmTkWA4u2\nMvNmyvekF33/gvZrD416mXlsr8ewJGTm5F6PYRHsRFkTqZPG2laPUdZNWlSHAJstxvkaXocCd1HC\n2QOBpYH7KOsgnTXAeZIkSZIkSRqFXANIkiSNGNcAknrDudil3vHzJ/WGnz2pN/zsSb3hGkCSJEmS\nJEmSJEkvUwZAkiRJkiRJkiRJfcYASJIkSZIkSZIkqc8YAEmSJEmSJEmSJPUZAyBJkiRJkiRJkqQ+\nYwAkSZIkSZIkSZLUZwyAJEmSJEmSJEmS+owBkCRJkiRJkiRJUp8xAJIkSZIkSZIkSeozBkCSJEmS\nJEmSJEl9ZpleD0CSJPWvq06fxNy5T/R6GJIkSZIkSS87VgBJkiRJkiRJkiT1GQMgSZIkSZIkSZKk\nPmMAJEmSJEmSJEmS1GcMgCRJkiRJkiRJkvqMAZAkSZIkSZIkSVKfMQCSJEmSJEmSJEnqMwZAkiRJ\nkiRJkiRJfcYASJIkSZIkSZIkqc8YAEmSJEmSJEmSJPUZAyBJkiRJkiRJkqQ+s0yvByBJkvrXrkdc\n2eshLJbzj5rQ6yFIkiRJkiQtEiuAJEmSJEmSJEmS+owBkCRJkiRJkiRJUp8xAJIkSZIkSZIkSeoz\nBkCSJEmSJEmSJEl9xgBIkiRJkiRJkiSpzxgASZIkSZIkSZIk9RkDIEmSJEmSJEmSpD5jACRJkiRJ\nkiRJktRnDIAkSZIkSZIkSZL6jAGQJEmSJEmSJElSnzEAkiRJkiRJkiRJ6jMGQJIkSZIkSZIkSX3G\nAEiSJEmSJEmSJKnPGABJkiRJkiRJkiT1mWV6PQBJiyci1gfub3rr5szcNiK2BW7scNozwDzgx8BZ\nmfndIfa5OrAvsCewIbAm8ChwO/ANYGZmzm855yZgm5am5gN/Bu4GvpiZs1rOab6GGzJz+wHGNBlo\nnH9AZs4Y5BrmD7S/yQGZOaPD+Dv5Wmbu36HfFYAn68t5wFqZ+WyHY18FPASMA87JzEPq+4cAXwKO\nzsxTBhpIROwEXNNm1wvAX4H7gEuBMzLzqUHamg58Hvi3zDyjzf61gJuBTYDzgA+23gdjQURcAUxq\ns+tZFtznp2fmTR3OXx/4ELAL8BpgWeBXwOXAf2TmYwP0vQrwMLACcHBmntvmmDOAw5reevF+j4h5\nwCr1/ccyc9WIWJXyOWvneeAx4B7g68CXGz+ziJgDbNZ07AaZ+UCnsUuSJEmSJGl0MQCS+kcCFwEP\ntLx/N3BFy3srUR7s7gTsFBFTM/PibjqJiG1qP+vUPq+iPBRfD5gI7ArMjojJmflkmybOpAQfAMtR\nwqOJwMyImJ6ZZ3boepuIWD0zH+2wf0o342/xGLBQkNFiTt3OAG5q2Xd8hzbm0J1VgXcB3+uw/72U\n8Gc43Al8p+n1UrX/XYCTgQkRseOiBjYRsRpwHSX8+SpjNPxpcQ4ljGlYDtiA8nOZGBF7Z+YlzSdE\nxH7A2cCKwA3Af1H+W/sO4Dhgv4h4Z2b+rkOfe1HCn78BBwELBUDAbMpnaCtgxzb7G/dka6D3hzq2\nZssDr6UEXm+r13d03Xc25XM+FYgO45UkSZIkSdIoZQAk9Y97M/OENu/P6fA+EXEgpVLjcxExMzOf\nH6iDiHg9cC2lamCfzLywZf8rKQ/N3w+cBBzRppkzWqsIaoXCT4GTIuKrmfl4yzkPUx5E70YJYlrH\ntTwlyPgLJdzq1rxO35tW7SqKIuL4obTR4g/A2sBkOgdAUxj6NXVyR7txRsTRlIqWd1OCjZlDbTgi\nVqbcF5tSqkgO6oPwB+DszFwozIuIicDVwBciYlbjcxMRu1Cu/3fArs3nRsQ4SrDyGeDaiNi0w+ft\nA8D/UMK6D0XEGzPzp80HZOZsSsg6nfYBUKd78uEBfhf8M3AL8NGIOCsz/yczz677NscASJIkSZIk\nacxxDSDpZSwzzwd+C7wa+McuTjmPUjFwUGv4U9v7K3AgZUq6Q2sw0M045lGmx1oJ2KLNIddQpq3b\no0MTOwIrU6qRxooHKJVCk2o48BIRsQ6lImNEr6n+zP6zvpw41PMj4hWUMGRL4JuU6cheGL4Rjj51\nysSfUwK8LeDFqf2+Qplab2JrcJSZ8zPzs5R7+fWU6RNfIiI2oPzMrwcuq29/cIQu4yUy89Y6tmXo\nfqpDSZIkSZIkjWJWAEmaS1mnZIWBDoqIzShTTt3dLvxpyMxnIuIzlEBpBeCJLsfxXN0+3Wbf45Tp\nxXaIiFfW0KLZFOBB4DZg7y77Gw1mAScCWwPfb9k3mTL92yxG/pp+X7drDOWkWnl1OWV6s4uAaZ3C\nn4jYGfgYJTAZR1l/6pTm9adqpcldwJGUqeT2plRAfSAzZ0fEesDHgfdQQssXKGvrzKBUls1vamsS\ncDjwBsp9+EvK+lRnDlbp1qXfA//Egu/ZLpRA6PLWip0Wx1ECs9va7PsA5XtzLWX6uD8B74+Ij2Vm\nu8/FcHukbgf8XSBJkiRJkqSxwQBIehmLiHUp03Y9Ddw7yOG71+23B2s3M88b4jhWoQQeD9H+wTiU\nIGRnSpXKpU3nLkdZd+j8ofQ5SjQCoMksHABNAX7IgnBmJG1ct133FRHLAJcAO9Ttvp2ClYg4HDi9\ntn8B5X57L3B1RPxrZn6x5ZQjKBVfX6SELLdFxNqUqerWoKxpNYsyLeB7gf8AXkmZdpCIeA+lgubB\n2t+zlNDodOBVtJ+acKg2qtvG9+w9dXvtQCdl5h3AHR127wc8CVyVmc9FxEzgYMo1fmvxhjuwiFgW\nmFBf3j2SfUmSJEmSJGnJMACSXoYiYiXgzZQH58sBJ2bmk4Oc1ggJfraY3U+PiHn130tTHujvVsex\nW2Y+1+G8KylVQnvQFAAB2wOr1ve2GuJYVo2IEwbY/3BjHZSRkJn3RMQ9lGt6MZSIiPHAO4GPjlTf\nTX2tCRxWX1420LFNlqJU0+xWX984QPizCXAaJfTYrrG+U0QcS1lz5vMRcXXLulBrAJGZv2lq5xRg\nPWDPzJzZ9P6pwC+AfagBEOV7OR/YMjP/2NTfT4BDIuLozHymy2ttd037UwKg+yhrVwH8fd3+chHb\nfFtt89LMbFTNfYsSAB3ECAVAEbEipVrvBGBDSvj045HoS5IkSZIkSUuWAZDU/6ZFxLQO+54EW5Qw\nqgAAIABJREFUTgU+1UU7a9Xtn1t3RMQESmDR6qbMvKnlvcPaHAeQlOqM9jszH42Im4CdI2K5pgf4\nUyiVQ7cy9ABoFeD4AfbfDYxYAFTNAo6JiM2b1o3ZgxKyzGRBsLC43tISdi1N+X7vCqwJfD0zr++y\nrSMp051dT1kv5t8j4obMvK/NsfvXvo5uhD8AmflERJxECe7eD3ym6Zy7msOf6jLg15Tv14sy896I\neJgF9yeU793SlOnmrq3HPRkR2wKPDyH8OaS23bAi8CZK6Pg0cHDTtHOr1m23Ux622q9um6dXvIVy\nb28bERtl5q8Xse2GzSJifod9L9S+D13MPiRJkiRJkjRKGABJ/e9uypRZUB5gTwKCsqbO1Mx8tMt2\nGset1mbfBOCTHc67qeX1Bo1qj4hYihLC/DNwBjAzIg7IzBkd2ppFefi+PfDdOg3ZJOBrmTk/Irq7\nkgV+m5nrD/WkYTYLOIYyDVwjAJoC/CgzfxcRwxUAbVG/Gp4H5lEqWL4JDGXavrUpFVl7AkcBnwa+\nGRFbt6ngavS5c0S8vWXfmnW7ecv797d2mJm3UaaCW6WuFfRaSuXKW+t4Hm86/BzgXcDsWmF1DfBd\n4OYBKszaObjl9VPA/1KmlTs9M5unSvtT3bb7fAyorqW0F/BYHScA9Z6+kBK4/QvwiaG23eIPLAg0\nl6WEd28D7gF2z8xFql7qd+PHr9zrIUiLzPtX6h0/f1Jv+NmTesPPnjR6GQBJ/W9OZp7QeBERn6Q8\nwH4fcH5ETOnyoXjjofzGrTsy8xhKiNHoY3fg8sEazMwXKBVFsyNiCiWM+Cwwo8Mpl1PWhdmD8qB8\nArA6pVJmTMrMORHxG8o1HRcRq1PCi48Pc1fnZOYhw9TW94C9MvPZiDiZEsJtSammOrbl2EZlzPQB\n2lu95fVC0xHWaQs/BxwALF/ffpASMG5KqfoBIDMvjojHgMOBbev2cOCROv1bt+tFvampKmswjYql\njSnhalsRsTSwUUvYsisLgqOnOgSZ0yLi2E5T7XXp4ebfBXU8x1DWoZoZEdtk5kIVfi93c+cualGX\n1Fvjx6/s/Sv1iJ8/qTf87Em94WdP6o1ug9elBj9EUj+pYc+BlL/6n0R5+NuNK+t28giN62fAXGDd\nugZOu2P+AHwfmFQfpL8X+D3ww5EY0xI0C3hDRLyW8jNZhtEdal3TmEat3k/7A88AR0fE/2k59i91\nu0ZmjuvwtV0XfZ4DHAJcTJlucLXMfE1mTqNMx/YSmTk7M3dgwRpT5wIrA+dFxNZDvuLBza7bHQY5\nbgKQEXFV03sfqNtLKdfZ+vUAZf2jnYdrsA2ZeRKlQvCNlCquccPdhyRJkiRJknrDAEh6GcrMv1Ee\nOj8PHBkRg66dk5m3Aj8GtoiIfQc5fMi/WyJiWeCVlLVI/jLAobOA8cA7gN2BmU3rsIxVjXVt9qCE\nWrdm5oM9HM+Q1PDuBMq6OxfUap2Gn9TtW1rPi4jNIuK0iHj3QO3Xqf72An6VmdMy85bMnFf3rUFZ\n/2dcfT0uIo6MiE/UsT2RmVdl5sEsqKpqnYpuOFxPCSN3i4g3DnBcYw2s6+p4xwM7UaZn2zszD2n9\nAk6v5xw0AuMG+BDwR+A9wAdHqA9JkiRJkiQtYQZA0stUZt4BnEn5PXBufcg+mH2BvwFfjoiD6xo+\nL6oP3/eo7UIJc7r1EeAVwHWZudAUYE0uA+YDJ1Me/F86hD5Gq9uA3wF7U9Y3GovXdBpwO7Ah8IWm\n979O+XmdVsMaACJiBcp0fh+jBH8DeYFS5bNSRLyiqY3lahtLUda0oYaBuwPHtwli1q/b3w7lwrqR\nmU8BH61j+U5EbNa8PyKWjYhTKVU8vwS+XHftXcd+8QDTu32Lcv0TI2LdERj7XMoUeQCnRsTaw92H\nJEmSJEmSljzXAJJe3o4DplCmf/oYJVRprOGzOXBF8xoomXlPRGxDmYbrbOCYiLgeeJhSlfNu4B+A\n5yghwOksbHpEzGt6vTxlSq+tgceAIwYacGY+FBG3A1tRKi5+MNDxEbE5JRCYk5lXtOxeNSJOGOh8\n4N7MvGiQYzr1vTElNPtVZl7Q6bjMnB8Rl1FCMOh++rcPRcROHfZ9KzPP7X60LxURU4FNgIsy897B\njs/M5yNiGnAXcEBEXJWZl2fmnRFxIuVe+3md+uwvlHVvNqKEG1d2bLi0/UJEXEipgLkzIq6m3Dc7\nA68B/gSsEREr1vDwk5R1in4QEZcCjwCbUSpt7qSEiI3rPApYATilhjiLLDMviohXUdYquqt+Nn5C\nWQdp23q9DwC7NIWcjenfBro/Ho2IbwN7UtZA+uzijLNDH9+oP7/tKJ/d9w13H5IkSZIkSVqyrACS\nXsYy86/Ah+vLYyNio/rv3YHjKSFQ6zl3AJsC/0JZR2gCpXpgd0og82lgw8w8rLbf6rDaduNrOrAm\nZa2TN2fmz7sYemPKtMu6mP5t89rP7m32rdIylnZfU7sYTycb1zYGmzIPFlzT7ZnZbYXKBsA2Hb42\nHNpQFzKVMvZNuj0hM++hBD1QqsTWre8fTwkas7Z7EPA45d6b1uUUfocBp1CCn3+lBEg/pwQrZ9Vj\nJtb+bqTcl7dQQp9/A14LnApMyMxnm9o9ql7nCt1e50Ay83RgC+CrwN8DBwP7UEKv44BNM/M+gIh4\nXT32l5l5+yBNf7VuDxzBdXoOBZ4C9oqIiSPUhyRJkiRJkpaQcfPnj/WlM6SXt4hYH7gfuDIz24Uc\ni9ru5cAlmXnhcLXZKxFxGPD6ug7Mku77fcA+mTlpSfe9uCLiRuC0zLym12MZKRGxNCX0+LtBph4c\ndSJiOvB54IDMnFHfmwfMy8z1h7GfK4BJwAaZ+cBQz9/1iCvH9P9onH/UhF4PQVok48evzNy5T/R6\nGNLLkp8/qTf87Em94WdP6o3x41fu6g+ErQCStJBaubENcHevx7K46tpGU+jBtdRKjff1ou/FFREb\nAFsCP+v1WEbYnsD9Yy38kSRJkiRJkgbjGkBS/9ikrmfzQKMaYDHsB3w6M3+x2KPqvbdRKqS+3IO+\nX0cJ2k/rQd+L60BgemY+1OuBjLC9WLAOz5hQ133aqn6101jb6qnMPGUx+jkEWIchTAMoSZIkSZKk\n0cMASOofQVnL5GZgxuI0lJljMbBoKzNvpnxPetH3L2i/9tCol5nH9noMS0JmTu71GBbBTpQ1kTpp\nrG31GGXdpEV1CLDZYpwvSZIkSZKkHjIAksa4uibHSC0KL2mUyczpwPQO+1Ydxn42H662JEmSJEmS\ntOS5BpAkSZIkSZIkSVKfMQCSJEmSJEmSJEnqMwZAkiRJkiRJkiRJfcYASJIkSZIkSZIkqc8YAEmS\nJEmSJEmSJPUZAyBJkiRJkiRJkqQ+YwAkSZIkSZIkSZLUZwyAJEmSJEmSJEmS+owBkCRJkiRJkiRJ\nUp8xAJIkSZIkSZIkSeozy/R6AJIkqX9ddfok5s59otfDkCRJkiRJetmxAkiSJEmSJEmSJKnPGABJ\nkiRJkiRJkiT1GQMgSZIkSZIkSZKkPmMAJEmSJEmSJEmS1GcMgCRJkiRJkiRJkvqMAZAkSZIkSZIk\nSVKfMQCSJEmSJEmSJEnqMwZAkiRJkiRJkiRJfcYASJIkSZIkSZIkqc8YAEmSJEmSJEmSJPWZZXo9\nAEmS1L92PeLKJd7n+UdNWOJ9SpIkSZIkjTZWAEmSJEmSJEmSJPUZAyBJkiRJkiRJkqQ+YwAkSZIk\nSZIkSZLUZwyAJEmSJEmSJEmS+owBkCRJkiRJkiRJUp8xAJIkSZIkSZIkSeozBkCSJEmSJEmSJEl9\nxgBIkiRJkiRJkiSpzxgASZIkSZIkSZIk9RkDIEmSJEmSJEmSpD5jACRJkiRJkiRJktRnDIAkSZIk\nSZIkSZL6jAGQJEmSJEmSJElSnzEAkiRJkiRJkiRJ6jPL9HoAkl6+ImJ94P6mt27OzG0jYlvgxg6n\nPQPMA34MnJWZ3x1in6sD+wJ7AhsCawKPArcD3wBmZub8lnNuArZpaWo+8GfgbuCLmTmr5Zzma7gh\nM7cfYEyTgcb5B2TmjEGuYf5A+5sckJkzOoy/k69l5v4d+l0BeLK+nAeslZnPdjj2VcBDwDjgnMw8\npL5/CPAl4OjMPGWggUTETsA1bXa9APwVuA+4FDgjM58apK3pwOeBf8vMM9rsXwu4GdgEOA/4YOt9\nMFpExO7A5U1vvfgzi4grgElN+96UmXMiYg6wWZvm5gNPAL8FrgJOzsy/1LbOAA5rOnbQe1OSJEmS\nJEmjhwGQpNEggYuAB1revxu4ouW9lSgPsncCdoqIqZl5cTedRMQ2tZ91ap9XUcKf9YCJwK7A7IiY\nnJlPtmniTErwAbAcJTyaCMyMiOmZeWaHrreJiNUz89EO+6d0M/4WjwELBRkt5tTtDOCmln3Hd2hj\nDt1ZFXgX8L0O+99LCX+Gw53Ad5peL1X73wU4GZgQETsuamATEasB11HCn68yisOf6l7gU5T7+OAO\nxzTu1Ydb3j8VaA7LlgJeTbn3PwG8MyLelZnPAbNrG1sBOw7b6CVJkiRJkrREGABJGg3uzcwT2rw/\np8P7RMSBlEqNz0XEzMx8fqAOIuL1wLXA88A+mXlhy/5XAucA7wdOAo5o08wZmflAy3mrAj8FToqI\nr2bm4y3nPEx5UL8bJYhpHdfylCDjL5Rwq1vzOn1vWrWr2oiI44fSRos/AGsDk+kcAE1h6NfUyR3t\nxhkRR1Mqt95NCZxmDrXhiFiZcl9sCnwdOGiUhz9k5r3ACRGxOZ0DoIXu1eqUzJzX+mZErAncBryd\n8nO9JDNnUwLR6RgASZIkSZIkjTmuASRpTMrM8ynTVr0a+McuTjkPWJ7ygP/C1p2Z+VfgQMqUdIfW\nYKCbccyjTMe1ErBFm0OuoUxbt0eHJnYEVqZUI40VD1AqhSZFxEJVPhGxDvA2Rvia6s/sP+vLiUM9\nPyJeAVwNbAl8kzLF2QvDN8KxIzP/CJxVX27Xy7FIkiRJkiRpeFgBJGksmwu8BlhhoIMiYjPKNFZ3\ntwt/GjLzmYj4DCVQWoGyNko3nqvbp9vse5wyvdgOEfHKGlo0mwI8SKm+2LvL/kaDWcCJwNbA91v2\nTaZM/zaLkb+m39ftGkM5qVZeXQ68gzIt4LRO4U9E7Ax8jBLwjaOsP3VK8/pTtRrnLuBIylRye1Mq\noD6QmbMjYj3g48B7KKHlC8CvKFVhZzRXHUXEJOBw4A2U+/CXlPWpzhys0m0xPVK3A36eJEmSJEmS\nNDZYASRpTIqIdSnTdj1NWRNlILvX7bcHazczz8vMj2fm3C7HsQol8HiIEuK0M4vyUP0lVSoRsRxl\n7ZUhT102Csyq28lt9k0BfsiCcGYkbVy3XfcVEcsAlwA71O2+nYKViDicsv7QxsAFwFeADYCrI+LD\nbU45gjIl3ReBO4DbImJtylR1B1PCozNqvxsA/wF8sqm/9wCXAf9Q+/sSZb2p04HTur3GRbRT3d49\nwv1IkiRJkiRpCbACSNKYEhErAW+mPDhfDjgxM58c5LRGSPCzxex+ekQ01k9ZmlJ1slsdx26Z+VyH\n866kVAntAVza9P72wKr1va2GOJZVI+KEAfY/nJlnD7HNrmXmPRFxD+WaXlwvKSLGA+8EPjpSfTf1\ntSZwWH15WZenLUWpptmtvr5xgPBnE0rocgewXWN9p4g4FrgF+HxEXN2y1s4aQGTmb5raOQVYD9gz\nM2c2vX8q8AtgH8q6U1C+l/OBLeu0bI3+fgIcEhFHZ+YzXV7roCJiWeDvgQOAfYH/Ab48XO1LkiRJ\nkiSpdwyAJI1m0yJiWod9TwKnAp/qop216vbPrTsiYgIlsGh1U2be1PLeYW2OA0jgVZ06z8xHI+Im\nYOeIWK7pAf4USuXQrQw9AFoFOH6A/XcDIxYAVbOAYyJi88ycU9/bgxKyzKQEC8PhLS1h19KU7/eu\nwJrA1zPz+i7bOhJYG7ge2Ab494i4ITPva3Ps/rWvoxvhD0BmPhERJ1GCu/cDn2k6567m8Ke6DPg1\nC6qmGu3cGxEPs+D+hPK9W5oy3dy19bgnI2Jb4PFhCH/+HBGd9t0KHJiZ3U59KEm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d66b0f4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the different results\n",
    "\n",
    "all_preds=np.array(all_preds)\n",
    "\n",
    "#plt.plot(figsize=(2000,100))\n",
    "#plt.show(block=False)\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ind=np.arange(1,len(all_preds)+1)\n",
    "plt.barh(ind, all_preds)\n",
    "\n",
    "ax.set_yticks(ind)\n",
    "ax.set_yticklabels(all_names, fontsize=20)\n",
    "\n",
    "ax.set_xlim([0, 5])\n",
    "ax.set_xlabel('Rmse', fontsize=30)\n",
    "ax.set_title('Rmses over different StackNet structures', fontsize=30)\n",
    "\n",
    "#fig.canvas.flush_events()\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "#plt.ylabel(\"RMSE\", fontsize=30)\n",
    "#plt.xlabel(\"Model name\", fontsize=30)\n",
    "#plt.subplots()[1].set_xticklabels(all_names)\n",
    "#plt.title(\"Rmses over different StackNet structures \", fontsize=30)\n",
    "#plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "#plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================== Start of Level 0 ======================\n",
      "Input Dimensionality 10 at Level 0 \n",
      "6 models included in Level 0 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1/4 , model 0 , rmse===0.428845 \n",
      "Fold 1/4 , model 1 , rmse===1.398365 \n",
      "Fold 1/4 , model 2 , rmse===1.446256 \n",
      "Fold 1/4 , model 3 , rmse===0.247155 \n",
      "Fold 1/4 , model 4 , rmse===0.219608 \n",
      "Fold 1/4 , model 5 , rmse===0.428924 \n",
      "=========== end of fold 1 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 2/4 , model 0 , rmse===0.448941 \n",
      "Fold 2/4 , model 1 , rmse===2.854982 \n",
      "Fold 2/4 , model 2 , rmse===1.383854 \n",
      "Fold 2/4 , model 3 , rmse===0.245070 \n",
      "Fold 2/4 , model 4 , rmse===0.186362 \n",
      "Fold 2/4 , model 5 , rmse===0.448593 \n",
      "=========== end of fold 2 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 3/4 , model 0 , rmse===0.465667 \n",
      "Fold 3/4 , model 1 , rmse===2.295900 \n",
      "Fold 3/4 , model 2 , rmse===1.475647 \n",
      "Fold 3/4 , model 3 , rmse===0.269993 \n",
      "Fold 3/4 , model 4 , rmse===0.229976 \n",
      "Fold 3/4 , model 5 , rmse===0.466005 \n",
      "=========== end of fold 3 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 4/4 , model 0 , rmse===0.451505 \n",
      "Fold 4/4 , model 1 , rmse===1.716726 \n",
      "Fold 4/4 , model 2 , rmse===1.404690 \n",
      "Fold 4/4 , model 3 , rmse===0.245913 \n",
      "Fold 4/4 , model 4 , rmse===0.222462 \n",
      "Fold 4/4 , model 5 , rmse===0.451363 \n",
      "=========== end of fold 4 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output dimensionality of level 0 is 6 \n",
      "====================== End of Level 0 ======================\n",
      " level 0 lasted 45.828530 seconds \n",
      "====================== Start of Level 1 ======================\n",
      "Input Dimensionality 6 at Level 1 \n",
      "1 models included in Level 1 \n",
      "Fold 1/4 , model 0 , rmse===0.197477 \n",
      "=========== end of fold 1 in level 1 ===========\n",
      "Fold 2/4 , model 0 , rmse===0.182491 \n",
      "=========== end of fold 2 in level 1 ===========\n",
      "Fold 3/4 , model 0 , rmse===0.207783 \n",
      "=========== end of fold 3 in level 1 ===========\n",
      "Fold 4/4 , model 0 , rmse===0.202346 \n",
      "=========== end of fold 4 in level 1 ===========\n",
      "Output dimensionality of level 1 is 1 \n",
      "====================== End of Level 1 ======================\n",
      " level 1 lasted 0.010003 seconds \n",
      "====================== End of fit ======================\n",
      " fit() lasted 45.838533 seconds \n",
      "====================== Start of Level 0 ======================\n",
      "1 estimators included in Level 0 \n",
      "====================== Start of Level 1 ======================\n",
      "1 estimators included in Level 1 \n"
     ]
    },
    {
     "data": {
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S3jN+ZJGalLjPeE9b7QNU5PxY1NhUgIYXfoYKwndHvQfj4px7An0nXoCGHoe1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\nkqSZMXCSCmhsuLRtR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Cd3+G9jjNsKV500uRBCOfBZ4AwgCXwY+Dl+HquITPA+fgI/j1Uk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\nSZIkSZIkqa0MnCRJkiRJktRWPwEneILdsmXjhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169fd175c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Use Pipelines\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.decomposition.pca import PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.feature_selection import SelectPercentile, f_regression\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Read data\n",
    "def keras_model():\n",
    "    # Here's a Deep Dumb MLP (DDMLP)\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128, input_dim=10))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    # we'll use categorical xent for the loss, and RMSprop as the optimizer\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    return model\n",
    "\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=1\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Pipeline([('std',MinMaxScaler()),('Ridge',Ridge(alpha=0.001, normalize=True, random_state=1234))]) ,\n",
    "    Pipeline([('pca',PCA(n_components=10, random_state=1)),('GB',GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1))]), \n",
    "    Pipeline([('fref',SelectPercentile(score_func=f_regression, percentile=99)),('ET', ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ))]),\n",
    "    Pipeline([('std',StandardScaler()),('mlp',MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,beta_1=0.1, beta_2=0.1, epsilon=0.1))]),\n",
    "    Pipeline([('std',StandardScaler()),('keras', KerasRegressor(build_fn=keras_model, epochs=10, batch_size=15, verbose=0))]),\n",
    "    Pipeline([('std',StandardScaler()),('Ridge',Ridge())])\n",
    "    #PCA(n_components=1, random_state=1)\n",
    "    \n",
    "    ],\n",
    "     \n",
    "        #2ND level # \n",
    "\n",
    "        [ Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds9=model.predict(X_test)\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds9,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds9)[0],np.sqrt(mean_squared_error(y_test,preds9)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30)\n",
    "plt.xlabel(\"Test target\", fontsize=30)\n",
    "plt.title(\"Scatter plot of [R,GBM,ET,MLP,Keras,R][R] All pipes StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds9)))\n",
    "all_names.append(\"Scatter plot of [R,GBM,ET,MLP,Keras,R][R] All pipes StackNet \") \n",
    "\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================== Start of Level 0 ======================\n",
      "Input Dimensionality 10 at Level 0 \n",
      "6 models included in Level 0 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1/4 , model 0 , rmse===0.428959 \n",
      "Fold 1/4 , model 1 , rmse===0.646961 \n",
      "Fold 1/4 , model 2 , rmse===1.446256 \n",
      "Fold 1/4 , model 3 , rmse===0.247155 \n",
      "Fold 1/4 , model 4 , rmse===0.237194 \n",
      "Fold 1/4 , model 5 , rmse===0.428924 \n",
      "=========== end of fold 1 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 2/4 , model 0 , rmse===0.448474 \n",
      "Fold 2/4 , model 1 , rmse===0.775299 \n",
      "Fold 2/4 , model 2 , rmse===1.383854 \n",
      "Fold 2/4 , model 3 , rmse===0.245070 \n",
      "Fold 2/4 , model 4 , rmse===0.250680 \n",
      "Fold 2/4 , model 5 , rmse===0.448593 \n",
      "=========== end of fold 2 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 3/4 , model 0 , rmse===0.466133 \n",
      "Fold 3/4 , model 1 , rmse===0.770484 \n",
      "Fold 3/4 , model 2 , rmse===1.475647 \n",
      "Fold 3/4 , model 3 , rmse===0.269993 \n",
      "Fold 3/4 , model 4 , rmse===0.211689 \n",
      "Fold 3/4 , model 5 , rmse===0.466005 \n",
      "=========== end of fold 3 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 4/4 , model 0 , rmse===0.451317 \n",
      "Fold 4/4 , model 1 , rmse===0.769216 \n",
      "Fold 4/4 , model 2 , rmse===1.404690 \n",
      "Fold 4/4 , model 3 , rmse===0.245913 \n",
      "Fold 4/4 , model 4 , rmse===0.260770 \n",
      "Fold 4/4 , model 5 , rmse===0.451363 \n",
      "=========== end of fold 4 in level 0 ===========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mimar\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output dimensionality of level 0 is 6 \n",
      "====================== End of Level 0 ======================\n",
      " level 0 lasted 49.187016 seconds \n",
      "====================== Start of Level 1 ======================\n",
      "Input Dimensionality 6 at Level 1 \n",
      "1 models included in Level 1 \n",
      "Fold 1/4 , model 0 , rmse===0.203831 \n",
      "=========== end of fold 1 in level 1 ===========\n",
      "Fold 2/4 , model 0 , rmse===0.209815 \n",
      "=========== end of fold 2 in level 1 ===========\n",
      "Fold 3/4 , model 0 , rmse===0.198536 \n",
      "=========== end of fold 3 in level 1 ===========\n",
      "Fold 4/4 , model 0 , rmse===0.206079 \n",
      "=========== end of fold 4 in level 1 ===========\n",
      "Output dimensionality of level 1 is 1 \n",
      "====================== End of Level 1 ======================\n",
      " level 1 lasted 0.014001 seconds \n",
      "====================== End of fit ======================\n",
      " fit() lasted 49.201017 seconds \n",
      "====================== Start of Level 0 ======================\n",
      "1 estimators included in Level 0 \n",
      "====================== Start of Level 1 ======================\n",
      "1 estimators included in Level 1 \n"
     ]
    },
    {
     "data": {
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85py7JtPnKglEZAcCDcifUi244+GcOyuNZFXRedpeoWN7Epi2J0U0WvQjREfXrYxuMp6K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\n1YDzKLvm/RfYMTPvbXS5rS4D/B1DGVLtY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1698e926a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Use Pipelineds an GRIDSEARCH\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from pystacknet.pystacknet import StackNetRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.decomposition.pca import PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import pearsonr\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.feature_selection import SelectPercentile, f_regression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "\n",
    "# Read data\n",
    "def keras_model():\n",
    "    # Here's a Deep Dumb MLP (DDMLP)\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128, input_dim=10))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Activation('relu'))\n",
    "    #model.add(Dropout(0.15))\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('linear'))\n",
    "\n",
    "    # we'll use categorical xent for the loss, and RMSprop as the optimizer\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    return model\n",
    "\n",
    "\n",
    "#PARAMETERS\n",
    "\n",
    "FOLDS=4\n",
    "METRIC=\"rmse\"\n",
    "RESTACKING=False\n",
    "RETRAIN=True\n",
    "SEED=12345\n",
    "VERBOSE=1\n",
    "\n",
    "models=[\n",
    "    \n",
    "    #1ST level #\n",
    "    [Pipeline([('std',MinMaxScaler()), ('Ridge',GridSearchCV(Ridge(alpha=0.001, normalize=True, random_state=1234),  {'alpha':(0.001, 1.), 'normalize':[True, False]}, cv=5) )]) ,\n",
    "    Pipeline([('pca',PCA(n_components=10, random_state=1)),('GB',GradientBoostingRegressor(n_estimators=200,learning_rate=0.05,max_features=0.2,min_samples_leaf=20,max_depth=6,random_state=1))]), \n",
    "    Pipeline([('fref',SelectPercentile(score_func=f_regression, percentile=99)),('ET', ExtraTreesRegressor(n_estimators=200, max_features=0.5, max_depth=15, random_state=1234 ))]),\n",
    "    Pipeline([('std',StandardScaler()),('mlp',MLPRegressor(hidden_layer_sizes=(100,50 ), activation=\"relu\", solver=\"adam\",alpha=0.01,batch_size=30, learning_rate=\"adaptive\",learning_rate_init=0.001, power_t=0.5,max_iter=20, shuffle=True, random_state=1, tol=0.0001, momentum=0.9,validation_fraction=0.1,beta_1=0.1, beta_2=0.1, epsilon=0.1))]),\n",
    "    Pipeline([('std',StandardScaler()),('keras', KerasRegressor(build_fn=keras_model, epochs=10, batch_size=15, verbose=0))]),\n",
    "    Pipeline([('std',StandardScaler()),('Ridge',Ridge())])\n",
    "    #PCA(n_components=1, random_state=1)\n",
    "    \n",
    "    ],\n",
    "    \n",
    "        #2ND level # \n",
    "\n",
    "        [ Ridge(alpha=0.001, normalize=True, random_state=1234)],    \n",
    "\n",
    "]\n",
    "\n",
    "#MODEL STATEMENT\n",
    "model=StackNetRegressor(models, metric=METRIC, folds=FOLDS,\n",
    "    restacking=RESTACKING, use_retraining=RETRAIN, \n",
    "    random_state=SEED, verbose=VERBOSE)\n",
    "\n",
    "#MODEL FIT\n",
    "model.fit(X,y)\n",
    "\n",
    "#MODEL PREDICT\n",
    "preds10=model.predict(X_test)\n",
    "\n",
    "\n",
    "plt.scatter(y_test.reshape(-1,1), preds10,  label=(\"R=%.4f, rmse=%.4f\"%(pearsonr(y_test.reshape(-1,1),preds10)[0],np.sqrt(mean_squared_error(y_test,preds10)))))\n",
    "\n",
    "#lt.hist([y,y_test], normed=True, label=[\"train\", \"test\"], bins=30, color=[\"green\",\"red\"] )\n",
    "plt.ylabel(\"Predictions\", fontsize=30)\n",
    "plt.xlabel(\"Test target\", fontsize=30)\n",
    "plt.title(\"Scatter plot of [R,GBM,ET,MLP,Keras,R][R] Pipes + Gridsearch StackNet \", fontsize=30)\n",
    "\n",
    "all_preds.append(np.sqrt(mean_squared_error(y_test,preds10)))\n",
    "all_names.append(\"Scatter plot of [R,GBM,ET,MLP,Keras,Pipe][R] Pipes + Gridsearch  StackNet \") \n",
    "\n",
    "\n",
    "plt.legend( loc = 'upper left', prop={'size': 30})\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
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 },
 "nbformat": 4,
 "nbformat_minor": 1
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